GorllaS.DA5030.Project.Rmd
Sanjana Gorlla
08/07/2022
Breast cancer sub-type classification using proteomic data
Problem statement : Luminal A, Luminal B, HER2, Basal-like are the molecular subtypes of breast cancer that are typically recognized. Each one is related to various prognoses, therapies, and treatments.
Objective : The main objective of this project is to perform classification on breast cancer dataset based on the proteomic expression dataset to identify the subtype of breast cancer. A collection of proteins produced by cancer cells is known as the breast cancer proteome. I am examining if the cancer subtype can be correctly identified using only the proteome data for each patient. The target variable in the study is PAM50 mRNA which is used in breast cancer intrinsic subtyping based on gene expression. The variable is categorical presenting four subtypes (basal-like, HER2-enriched, Luminal A and Luminal B). PAMA50 mRNA proteins are my predictor variables.Using supervised ML classification, I am examining if the cancer subtype can be correctly identified using only the proteome data for each patient.
• Question 1 : where does the data come from?
This data collection includes 77 breast cancer samples from the Clinical Proteomic Tumor Analysis Consortium (NCI/NIH) that have had their iTRAQ proteome profiles published. For each sample, it comprises expression levels for around 12,000 proteins, with missing values present when a particular protein could not be measured. Kaggle URL : https://www.kaggle.com/datasets/piotrgrabo/breastcancerproteomes?resource=download
It has three different files : 77cancerproteomesCPTACitraq.csv, clinicaldatabreast_cancer.csv, PAM50_proteins.csv
File: 77cancerproteomesCPTACitraq.csv -RefSeqaccessionnumber: RefSeq protein ID (each protein has a unique ID in a RefSeq database) -gene_symbol: a symbol unique to each gene (every protein is encoded by some gene) -gene_name: a full name of that gene -Remaining columns: log2 iTRAQ ratios for each sample (protein expression data, most important), three last columns are from healthy individuals
File: clinicaldatabreast_cancer.csv -First column “Complete TCGA ID” is used to match the sample IDs in the main cancer proteomes file (see example script). -All other columns have self-explanatory names, contain data about the cancer classification of a given sample using different methods.
File: PAM50_proteins.csv -Contains the list of genes and proteins used by the PAM50 classification system. -The column RefSeqProteinID contains the protein IDs that can be matched with the IDs in the main protein expression data set.
- Provided installation statements for all packages
- Importing all required libraries
#install.packages("stabs")
# install.packages("factoextra")
# install.packages("NbClust")
# install.packages("ggfortify")
# install.packages("glmnet")
# install.packages("foreign")
# install.packages("ggplot2")
# install.packages("MASS")
# install.packages("Hmisc")
# install.packages("reshape2")
# install.packages("randomForest")
# install.packages("data.table")
# install.packages("mlr")
# intsall.packages("caret")
# install.pacakges("dplyr") # data manipulation
# install.packages("readr") # data input and manipulation
# install.packages("caret) #select tuning parameters
# install.packages("MASS") # contains the data
# install.packages("DataExplorer") #data set visualization
# install.packages("nnet") # used for Multinomial Classification
# install.packages("readr") #assist with text manipulation
# install.packages("kernlab") #assist with SVM feature selection
# install.packages("class") # used for an object-oriented style of programmin
# install.packages("KernelKnn") # used for K- Nearest-Neighbors method
# install.pacakges("nnet") # Used for Neural Net
# install.packages("e1071") #supports vector machine algorithm
# install.packages("forecast") # for model prediction
# install.pacakges("rpart") #construct recursive partitions for classification
# install.packages("neuralnet")
# install.packages("NbClust")
# install.packages("psych")
# install.packages("pheatmap")
# install.packages("OneR")
# install.packges("naivebayes")
# install.packages("dplyr")
# install.packages("ggplot2")
# install.packages("psych")
# install.packages("tidyverse")
# install.packages("assertr")
# install.packages("knitr")
# install.packages("psych")
# install.packages("caret")
# install.packages("e1071")
# install.packages("C50")
# install.packages("gmodels")
# install.packages("pROC")
# install.packages("caTools")- Importing all required libraries
#loading required libraries
library(stabs)
library(factoextra)
library(NbClust)
library(ggfortify)
library(glmnet)
require(foreign)
require(ggplot2)
require(MASS)
require(Hmisc)
require(reshape2)
library(randomForest)
library(data.table)
library(mlr)
library(caret)
library(dplyr) # data manipulation
library(readr) # data input and manipulation
library(caret) #select tuning parameters
library(MASS) # contains the data
library(DataExplorer) #data set visualization
library(nnet) # used for Multinomial Classification
library(readr) #assist with text manipulation
library(kernlab) #assist with SVM feature selection
library(class) # used for an object-oriented style of programmin
library(KernelKnn) # used for K- Nearest-Neighbors method
library(nnet) # Used for Neural Net
library(e1071) #supports vector machine algorithm
library(forecast) # for model prediction
library(rpart) #construct recursive partitions for classification
library(neuralnet)
library(NbClust)
library(psych)
library(pheatmap)
library(OneR)
library(naivebayes)
library(dplyr)
library(ggplot2)
library(psych)
library(pROC)
library(tidyverse)
library(assertr)
library(knitr)
library(psych)
library(caret)
library(e1071)
library(C50)
library(gmodels)
library(pROC)
library(caTools)
library(ggplot2)
library(GGally)
library(ipred)1.Data Acquisition and manipulation
- for importing data, I used the read.csv() function
- Later, I combined all the datasets into one
- Converted the patients id to one format
- used the head(), str(), dim(), glimpse(), summary() to explore the dataset
Data import - PAM50_proteins.csv
First steps: importing the data and getting the data set into a workable format. list of genes and proteins
#Importing data which has list of genes and proteins
gene_proteins <- read.csv("/Users/sanjanagorlla/Desktop/multiclass-classfication/BREAST-CANCER-SUBTYPE/PAM50_proteins.csv")
# 100 rows and 4 columns
head(gene_proteins)tail(gene_proteins)str(gene_proteins)## 'data.frame': 100 obs. of 4 variables:
## $ GeneSymbol : chr "MIA" "FGFR4" "FGFR4" "FGFR4" ...
## $ RefSeqProteinID: chr "NP_006524" "NP_002002" "NP_998812" "NP_075252" ...
## $ Species : chr "Homo sapiens" "Homo sapiens" "Homo sapiens" "Homo sapiens" ...
## $ Gene.Name : chr "melanoma inhibitory activity" "fibroblast growth factor receptor 4" "fibroblast growth factor receptor 4" "fibroblast growth factor receptor 4" ...dim(gene_proteins)## [1] 100 4glimpse(gene_proteins)## Rows: 100
## Columns: 4
## $ GeneSymbol <chr> "MIA", "FGFR4", "FGFR4", "FGFR4", "GPR160", "ACTR3B", …
## $ RefSeqProteinID <chr> "NP_006524", "NP_002002", "NP_998812", "NP_075252", "N…
## $ Species <chr> "Homo sapiens", "Homo sapiens", "Homo sapiens", "Homo …
## $ Gene.Name <chr> "melanoma inhibitory activity", "fibroblast growth fac…summary(gene_proteins)## GeneSymbol RefSeqProteinID Species Gene.Name
## Length:100 Length:100 Length:100 Length:100
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :characterData import - clinicaldatabreast_cancer.csv
main cancer dataset (has information about patients suffering from each sub-type classified based on PAM50.mRNA)
clinical <- read.csv("/Users/sanjanagorlla/Desktop/multiclass-classfication/BREAST-CANCER-SUBTYPE/clinical_data_breast_cancer.csv")
# 105 obseravtions and 30 columns
head(clinical)tail(clinical)summary(clinical)## Complete.TCGA.ID Gender Age.at.Initial.Pathologic.Diagnosis
## Length:105 Length:105 Min. :30.00
## Class :character Class :character 1st Qu.:49.00
## Mode :character Mode :character Median :58.00
## Mean :58.69
## 3rd Qu.:67.00
## Max. :88.00
##
## ER.Status PR.Status HER2.Final.Status Tumor
## Length:105 Length:105 Length:105 Length:105
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Tumor..T1.Coded Node Node.Coded Metastasis
## Length:105 Length:105 Length:105 Length:105
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Metastasis.Coded AJCC.Stage Converted.Stage Survival.Data.Form
## Length:105 Length:105 Length:105 Length:105
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Vital.Status Days.to.Date.of.Last.Contact Days.to.date.of.Death
## Length:105 Min. : 0.0 Min. : 160.0
## Class :character 1st Qu.: 240.0 1st Qu.: 947.5
## Mode :character Median : 643.0 Median :1364.0
## Mean : 788.4 Mean :1254.5
## 3rd Qu.:1288.0 3rd Qu.:1627.5
## Max. :2850.0 Max. :2483.0
## NA's :94
## OS.event OS.Time PAM50.mRNA
## Min. :0.0000 Min. : 0.0 Length:105
## 1st Qu.:0.0000 1st Qu.: 240.0 Class :character
## Median :0.0000 Median : 665.0 Mode :character
## Mean :0.1048 Mean : 817.6
## 3rd Qu.:0.0000 3rd Qu.:1305.0
## Max. :1.0000 Max. :2850.0
##
## SigClust.Unsupervised.mRNA SigClust.Intrinsic.mRNA miRNA.Clusters
## Min. :-12.000 Min. :-13.000 Min. :1
## 1st Qu.: -6.000 1st Qu.:-12.000 1st Qu.:3
## Median : -5.000 Median : -6.000 Median :4
## Mean : -4.886 Mean : -7.181 Mean :4
## 3rd Qu.: -3.000 3rd Qu.: -2.000 3rd Qu.:5
## Max. : 0.000 Max. : 0.000 Max. :7
##
## methylation.Clusters RPPA.Clusters CN.Clusters
## Min. :1.000 Length:105 Min. :1.00
## 1st Qu.:2.000 Class :character 1st Qu.:1.00
## Median :4.000 Mode :character Median :3.00
## Mean :3.343 Mean :2.59
## 3rd Qu.:4.000 3rd Qu.:3.00
## Max. :5.000 Max. :5.00
##
## Integrated.Clusters..with.PAM50. Integrated.Clusters..no.exp.
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :2.743 Mean :1.981
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000
##
## Integrated.Clusters..unsup.exp.
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.352
## 3rd Qu.:3.000
## Max. :5.000
## dim(clinical)## [1] 105 30clinical$PAM50.mRNA## [1] "Basal-like" "Basal-like" "Basal-like" "Basal-like"
## [5] "Basal-like" "Basal-like" "Basal-like" "Basal-like"
## [9] "Basal-like" "Basal-like" "Basal-like" "Basal-like"
## [13] "Basal-like" "Basal-like" "Basal-like" "Basal-like"
## [17] "Basal-like" "Basal-like" "Basal-like" "Basal-like"
## [21] "Basal-like" "Basal-like" "Basal-like" "Basal-like"
## [25] "Basal-like" "HER2-enriched" "HER2-enriched" "HER2-enriched"
## [29] "HER2-enriched" "HER2-enriched" "HER2-enriched" "HER2-enriched"
## [33] "HER2-enriched" "HER2-enriched" "HER2-enriched" "HER2-enriched"
## [37] "HER2-enriched" "HER2-enriched" "HER2-enriched" "HER2-enriched"
## [41] "HER2-enriched" "HER2-enriched" "HER2-enriched" "Luminal A"
## [45] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [49] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [53] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [57] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [61] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [65] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [69] "Luminal A" "Luminal A" "Luminal A" "Luminal A"
## [73] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [77] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [81] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [85] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [89] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [93] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [97] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [101] "Luminal B" "Luminal B" "Luminal B" "Luminal B"
## [105] "Luminal B"str(clinical)## 'data.frame': 105 obs. of 30 variables:
## $ Complete.TCGA.ID : chr "TCGA-A2-A0T2" "TCGA-A2-A0CM" "TCGA-BH-A18V" "TCGA-BH-A18Q" ...
## $ Gender : chr "FEMALE" "FEMALE" "FEMALE" "FEMALE" ...
## $ Age.at.Initial.Pathologic.Diagnosis: int 66 40 48 56 38 57 74 60 61 67 ...
## $ ER.Status : chr "Negative" "Negative" "Negative" "Negative" ...
## $ PR.Status : chr "Negative" "Negative" "Negative" "Negative" ...
## $ HER2.Final.Status : chr "Negative" "Negative" "Negative" "Negative" ...
## $ Tumor : chr "T3" "T2" "T2" "T2" ...
## $ Tumor..T1.Coded : chr "T_Other" "T_Other" "T_Other" "T_Other" ...
## $ Node : chr "N3" "N0" "N1" "N1" ...
## $ Node.Coded : chr "Positive" "Negative" "Positive" "Positive" ...
## $ Metastasis : chr "M1" "M0" "M0" "M0" ...
## $ Metastasis.Coded : chr "Positive" "Negative" "Negative" "Negative" ...
## $ AJCC.Stage : chr "Stage IV" "Stage IIA" "Stage IIB" "Stage IIB" ...
## $ Converted.Stage : chr "No_Conversion" "Stage IIA" "No_Conversion" "No_Conversion" ...
## $ Survival.Data.Form : chr "followup" "followup" "enrollment" "enrollment" ...
## $ Vital.Status : chr "DECEASED" "DECEASED" "DECEASED" "DECEASED" ...
## $ Days.to.Date.of.Last.Contact : int 240 754 1555 1692 133 309 425 643 775 964 ...
## $ Days.to.date.of.Death : int 240 754 1555 1692 NA NA NA NA NA NA ...
## $ OS.event : int 1 1 1 1 0 0 0 0 0 0 ...
## $ OS.Time : int 240 754 1555 1692 133 309 425 643 775 964 ...
## $ PAM50.mRNA : chr "Basal-like" "Basal-like" "Basal-like" "Basal-like" ...
## $ SigClust.Unsupervised.mRNA : int 0 -12 -12 -12 0 0 0 -12 -12 -12 ...
## $ SigClust.Intrinsic.mRNA : int -13 -13 -13 -13 -13 -13 -13 -13 -13 -13 ...
## $ miRNA.Clusters : int 3 4 5 5 5 5 3 5 2 5 ...
## $ methylation.Clusters : int 5 4 5 5 5 5 5 5 5 5 ...
## $ RPPA.Clusters : chr "Basal" "Basal" "Basal" "Basal" ...
## $ CN.Clusters : int 3 4 1 1 1 1 1 1 1 3 ...
## $ Integrated.Clusters..with.PAM50. : int 2 2 2 2 2 2 2 2 2 2 ...
## $ Integrated.Clusters..no.exp. : int 2 1 2 2 2 2 2 2 2 2 ...
## $ Integrated.Clusters..unsup.exp. : int 2 1 2 2 2 2 2 2 2 2 ...summary(clinical)## Complete.TCGA.ID Gender Age.at.Initial.Pathologic.Diagnosis
## Length:105 Length:105 Min. :30.00
## Class :character Class :character 1st Qu.:49.00
## Mode :character Mode :character Median :58.00
## Mean :58.69
## 3rd Qu.:67.00
## Max. :88.00
##
## ER.Status PR.Status HER2.Final.Status Tumor
## Length:105 Length:105 Length:105 Length:105
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Tumor..T1.Coded Node Node.Coded Metastasis
## Length:105 Length:105 Length:105 Length:105
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Metastasis.Coded AJCC.Stage Converted.Stage Survival.Data.Form
## Length:105 Length:105 Length:105 Length:105
## Class :character Class :character Class :character Class :character
## Mode :character Mode :character Mode :character Mode :character
##
##
##
##
## Vital.Status Days.to.Date.of.Last.Contact Days.to.date.of.Death
## Length:105 Min. : 0.0 Min. : 160.0
## Class :character 1st Qu.: 240.0 1st Qu.: 947.5
## Mode :character Median : 643.0 Median :1364.0
## Mean : 788.4 Mean :1254.5
## 3rd Qu.:1288.0 3rd Qu.:1627.5
## Max. :2850.0 Max. :2483.0
## NA's :94
## OS.event OS.Time PAM50.mRNA
## Min. :0.0000 Min. : 0.0 Length:105
## 1st Qu.:0.0000 1st Qu.: 240.0 Class :character
## Median :0.0000 Median : 665.0 Mode :character
## Mean :0.1048 Mean : 817.6
## 3rd Qu.:0.0000 3rd Qu.:1305.0
## Max. :1.0000 Max. :2850.0
##
## SigClust.Unsupervised.mRNA SigClust.Intrinsic.mRNA miRNA.Clusters
## Min. :-12.000 Min. :-13.000 Min. :1
## 1st Qu.: -6.000 1st Qu.:-12.000 1st Qu.:3
## Median : -5.000 Median : -6.000 Median :4
## Mean : -4.886 Mean : -7.181 Mean :4
## 3rd Qu.: -3.000 3rd Qu.: -2.000 3rd Qu.:5
## Max. : 0.000 Max. : 0.000 Max. :7
##
## methylation.Clusters RPPA.Clusters CN.Clusters
## Min. :1.000 Length:105 Min. :1.00
## 1st Qu.:2.000 Class :character 1st Qu.:1.00
## Median :4.000 Mode :character Median :3.00
## Mean :3.343 Mean :2.59
## 3rd Qu.:4.000 3rd Qu.:3.00
## Max. :5.000 Max. :5.00
##
## Integrated.Clusters..with.PAM50. Integrated.Clusters..no.exp.
## Min. :1.000 Min. :1.000
## 1st Qu.:2.000 1st Qu.:1.000
## Median :3.000 Median :2.000
## Mean :2.743 Mean :1.981
## 3rd Qu.:4.000 3rd Qu.:3.000
## Max. :4.000 Max. :4.000
##
## Integrated.Clusters..unsup.exp.
## Min. :1.000
## 1st Qu.:1.000
## Median :2.000
## Mean :2.352
## 3rd Qu.:3.000
## Max. :5.000
## Data import - 77_cancer_proteomes_CPTAC_itraq.csv
The following datset has information about the protein expression data
proteomes <- read.csv("/Users/sanjanagorlla/Desktop/multiclass-classfication/BREAST-CANCER-SUBTYPE/77_cancer_proteomes_CPTAC_itraq.csv")
# 12553 observations(proteins) and 86 columns
head(proteomes)tail(proteomes)dim(proteomes)## [1] 12553 86Combining the datasets
- Two data sets need to be combined before analysis
- using the cbind()
- new column was formed in each data set
- the two data sets were connected based on this new column
# Transposing the proteome matrix will result in rows rather than columns of observations.
## save rownames
# RefSeq_accession_number : ID of proteins
n <- proteomes$RefSeq_accession_number
#Transpose all columns except the first three
proteomes <- as.data.frame(t(proteomes[,4:86]))
colnames(proteomes) <- n
#Row names in the first column,
proteomes <- cbind(rownames(proteomes), data.frame(proteomes, row.names=NULL))
colnames(proteomes)[1] <- "Complete.TCGA.ID"Manipulating the format
working with the Patient IDs : - Unfortunately, the patient IDs in the clinical dataset and the proteomic data set have different formats. - To enable combining of the two data sets on this variable, this piece of code reformats the id in the clinical data set. - defined the code which does the job - used sapply() to implement the function defined
# To enable the combining of data sets, Complete.TCGA.ID is being reorganized into a clinical format.
# Defining the restructuring formula:
get.clinical.id <- function(proteome.id) {
x = substr(proteome.id, 4, 7)
y = substr(proteome.id, 0, 2)
paste("TCGA",y,x,sep="-")
}
#sapply to proteomes' id column
proteomes$Complete.TCGA.ID <- sapply(proteomes$Complete.TCGA.ID, get.clinical.id)
proteomes_all <- proteomes• Question 2 : how do you plan on assessing data quality and deal with missing values? ANSWER : I have explored the proteomes data set using head(), dim(), str(), dim(), glimpse(), summary() functions
• Question 3 : what strategy are you using for data imputation and why? if the data set has no missing data, can you randomly remove data and then impute the data and compare performance of your algorithms with imputed vs full data? why do they differ? how do they differ?
I have used plot intro() from the DataExplorer package to check if the missing values are present. There are 10% of missing values in the dataset.Discarded variables with more than 25% of the data missing because they wouldn’t be significant for further research. Used the mean imputation method, imputed the NA values with the mean of a specific column for the remaining variables with missing data.
Exploring the dataset merging and manipulating the datasets
After merging and manipulating the datasets retrieved - The proteome data set is the final one and to be used in the further analysis using the head str dim glimpse summary functions, the dimensions of the data can be observed
#head(proteomes)
#dim(proteomes)
#tail(proteomes)
#summary(proteomes)
#glimpse(proteomes)
#str(proteomes)• Question : how do you assess normality, distribution, skew – and does it matter for your algorithms? To predict the cancer sub-type correctly using the proteome data for each patient, I am going to implement following algorithms (SVM, Neural Networks, Naive Bayes and Random forests) : I am going to assess the distribution of the data using histograms and have built pair.panels() plot. The algorithms which I am going to use does not assume normality (SVM, Neural Networks,Naive Bayes and random forests) and works well regardless of the distribution. The dataset is reasonably well balanced, although HER2 is slightly underrepresented
Data Exploration
- exploratory data plots - plot_intro(), ggplots after the feature selection
- detection of outliers for continuous features - box(), defined outlier() to detect the outliers
- correlation/collinearity/chi-squared analysis - cor(),
- evaluation of distribution - barplots, histograms, boxplots for each subtype, pair.panels() plot
• what kinds of exploratory data analysis and visualization do you plan on doing?
2. Data Exploration
- I have used plot_intro function from DataExplorer package
- plot_intro provides an insight of what type of data is present along with that it provides the information about missing values
- plotted a graph to show the proportion of missing data for each variable.
- Apart from that, I have used str and summary to understand the structure of the data present
- Discarded variables with more than 25% of the data missing because they wouldn’t be significant for further research.
- Used the mean imputation method, imputed the NA values with the mean of a specific column for the remaining variables with missing data.
- After cleaning the data, using the ggplot() I have analyzed the distribution of each subtype in the dataset
- exploratory data plots: I used plot intro() from the DataExplorer package. plot intro gives an understanding of the sort of data present as well as information about missing values. In addition, I have used str() and summary() to comprehend the data’s structure.
#Visualizing structure of the data set
plot_intro(proteomes)
It is essential to explore the clinical dataset as it has all the information about the pateints
clinical$Age.at.Initial.Pathologic.Diagnosis <- as.numeric(clinical$Age.at.Initial.Pathologic.Diagnosis) # the histogram distribution would only work with numerical data
h1 <- hist(clinical$Age.at.Initial.Pathologic.Diagnosis)
b1<- barplot(table(clinical$ER.Status), col=c("blue", "red", "green"), main = "ER Status")
b2<- barplot(table(clinical$PR.Status), col=c("red", "green"), main = "PR Status")
b3<- barplot(table(clinical$HER2.Final.Status), col=c("blue", "red", "green"), main = "HER2 Final Status")
b4<- barplot(table(clinical$Tumor..T1.Coded), col=c("red", "green"), main = "Tumor--T1 Coded")
b5<- barplot(table(clinical$Node.Coded), col=c("red", "green"), main = "Node-Coded")
b6<- barplot(table(clinical$Metastasis.Coded), col=c("blue", "red", "green"), main = "Metastasis-Coded")
b7<- ggplot(clinical,aes(x= `PAM50.mRNA`,fill=`PAM50.mRNA`))+geom_bar()+theme(legend.position = "none")
b7
Missing Values
This plot_intro() plot shows that the dataset has missing observations. we can know the count of na’s from summary() and also colSums()
# colSums(is.na(proteomes))
# I have already used the sumarry() in the previous section• how do you plan on assessing data quality and deal with missing values? what strategy are you using for data imputation and why?
According to the plot we see that we have 10% of missing values in the dataset. Without suffering too much of a loss, we can exclude all variables with >25 percent missing data. Using the mean, the remaining missing data can be imputed (a more sophisticated form of imputation would be preferable but is quite computationally expensive and we dont have a huge amount of missing data, so I stuck with means in my analysis).
- Discarded variables with more than 25% of the data missing because they wouldn’t be significant for further research.
- Used the mean imputation method, we impute the NA values with the mean of a specific column for the remaining variables with missing data. (for which i have implemented the for-loop which did the job)
Step 1 - Missing data : The code below counts missing data by column and plots a graph to show the proportion of missing data for each variable.
#looking for proteomes with many NAs
naCounts <- colSums(is.na(proteomes)) / nrow(proteomes)
#plotting missing data proportions
plot(sort(naCounts, decreasing = TRUE), col ="blue", type = 'h', xlab = "index of proteome", ylab="proportion of missing data", main = "Propotion of missing data for each proteome") 
#how many have more than 25% missing data
length(naCounts[naCounts>0.25])## [1] 2251Without suffering too much loss, we can exclude all variables with >25% missing data. Using the mean, the remaining missing data can be imputed (a more sophisticated form of imputation would be preferable but is quite computationally expensive and we dont have a huge amount of missing data, so I stuck with means in my analysis).
Step 2:
#remove variables with >25% missing data
proteomes <- proteomes[ , colSums(is.na(proteomes)) / nrow(proteomes) < 0.25] #removing variables with >10% missing data
#loop to impute means for remaining missing data
for (i in which(sapply(proteomes, is.numeric))) {
proteomes[is.na(proteomes[, i]), i] <- mean(proteomes[, i], na.rm = TRUE)
}Now the dataset is clean, Lets explore if there are any missing values
dim(proteomes) # a total of 2251 variables are removed ## [1] 83 10303plot_intro(proteomes)
The proteome dataset is now clean, Therefore, I have now joined the proteome dataset and clinical datset using inner_join() from dplyr package.
#inner join on data to create full data set
data <- inner_join(clinical, proteomes, by = "Complete.TCGA.ID")
#replacing lengthy col name
colnames(data)[3] <- "diag_age"Exploring the final datset
dim(data)## [1] 80 10332#head(data)
#tail(data)
#str(data)Evaluation of distribution
The main idea of the project is to check if the proteomic datset can classify the subtype of breast cancer.Therefore, it is important to check the number of observations in each subtype The plot below shows how many patients have each subtype of breast cancer.
#Barplot of subtypes
ggplot(data, aes(PAM50.mRNA, col = PAM50.mRNA, fill = PAM50.mRNA, alpha=0.7)) + geom_bar() + ggtitle("Proportion of patients with each cancer subtype")+ geom_text(stat='count', aes(label=..count..), vjust=-1)
Therefore , They are reasonably well balanced, although HER2 is slightly underrepresented.
- Data Cleaning & Shaping
Data Imputation
- Data imputation is already done in previous chunks
- Mean imputation for proteome dataset is done
Proper Encoding of Data
- Encoding was done for only PAM50.mRNA column
- PAM50.mRNA is categorized into four types “Basal.like”, “HER2-enriched”, “Luminal.A”, “Luminal.B”
Normalization/Standardization
- Normalizing the data does not make any difference in the predictions as the protein expression data already ranges on -1 0 1 scale ### Feature engineering - PCA
- Principal component analysis is also done using prComp function
- Principal components are taken into consideration only when the cumulative variance is greater than 85%
- To get the cumulative variance of 85 or greater, I was forced to select 48 components
- But i want to know the list of features(proteins) important of the classification of breast cancer sub-type, Because of this I haven’t used Principal components for my models ### Feature selection - repeated lasso regression
- I selected variables using repeated lasso regression as my method. -A total of 30 proteins were selected over more than 20 times.
- These proteins are taken into consideration for further analysis
- Checked the distribution of cancer subtype using these proteins
################################
### Feature engineering: PCA ###
################################
#Performing PCA on the dataset
data_PCA <- prcomp(data[,31:ncol(data)], center = T, scale = T)
#Printing Principal components
#print(data_PCA)
#Summary of Principal components
#summary(data_PCA)
#str(data_PCA)
#Plotting variance plot of the Principal components
screeplot(data_PCA, type = "l", main = "Plot of the Principal Components")
• how will you select the features? will you use PCA? I will use repeated lasso regression to select the features. When the cumulative variance exceeds 85%, only principal components are taken into account. I had to choose 48 components for the cumulative variance to be 85 or higher. However, I’m curious about the list of characteristics (proteins) crucial to the classification of breast cancer subtypes. As a result, I haven’t employed principal components in my models.
• what kind of feature engineering will you use? will you add new derived features? I have used feature selection technique. No, i havent added any new features
• what do you plan on predicting? predict the breast cancer subtype using the set of proteins selected using feature selection(repeated lasso regression)
• what kind of normalization, standardization, regularization, or transformation do you plan on using and why? Normalizing the data does not make any difference in the predictions as the protein expression data already ranges on -1 0 1 scale
Feature selection : repeated lasso regression
The PAM50 genetic test is used to identify the subtype of breast cancer. A list of proteins linked to the PAM50 genes is included in this data collection. Therefore, it would appear likely that these are the best factors to utilize when categorizing breast cancer subtypes. To test if machine learning techniques might be used to find a set of proteins with as good or higher prediction power at classifying cancer subtypes, I chose an approach that was independent of biology. I’m selecting variables using repeated lasso regression as my method. The data set is reduced via lasso regression, which also creates a sparse set of predictor variables. However, because of the stochastic nature of the reduction, the findings are not always reliable. To get around this, I conducted 100 iterations of the lasso regression and prioritized the variables according to how frequently they were used in the final model.
# Defining a function that performs lasso regression again and returns the chosen model variables
LassoSub=function(k=1, Xdata, Ydata){
set.seed(k)
s=sample(nrow(data), size=0.8*nrow(data))
Xsub=Xdata[s, ]
Ysub=Ydata[s]
model.sub=cv.glmnet(x=Xsub, y=Ysub, alpha=1, family="multinomial") #cross validated lasso
coef.sub=coef(model.sub, s='lambda.1se')[-1] #using lambda +1se hyperparameter value for parsimony
return(coef.sub)
}options(warn = -1) #turn off warnings
#Run model 100 times and save results
niter=100
lasso.stab=sapply(1:niter, FUN=LassoSub, Xdata=as.matrix(data[,31:ncol(data)]), Ydata=as.matrix(data[,21]))
#create a matrix of all predictor variables
stability_matrix <- matrix(nrow=length(lasso.stab[[1]]),ncol=length(lasso.stab))
rownames(stability_matrix) <- rownames(lasso.stab[[1]])
#loop through to put list contents into matrix
for (i in 1:300){
temp.data.frame <- as.matrix(lasso.stab[[i]])
stability_matrix[,i] <- temp.data.frame
}
stability_matrix <- ifelse(stability_matrix != 0, 1, 0) #Replacing beta values with binary 1/0 (selected/not selected)
stability_matrix <- stability_matrix[2:nrow(stability_matrix),] #remove intercept value
stable_variables <- as.data.frame(rowSums(stability_matrix)) #create data frame with count of how many times each variable is selected for a model
stable_variables$protein <- rownames(stable_variables) #create column of variable names
colnames(stable_variables)[1] <- "times_selected" #assign appropriate column name
stable_variables <- stable_variables[!is.na(stable_variables$times_selected),] #remove NAs
stable_variables <- stable_variables[stable_variables$times_selected != 0,] #remove all variables that were never selected
stable_variables <- stable_variables[order(-stable_variables$times_selected),] #ordering by number of times selectedvisualizing the selected features
#plotting stable variables
ggplot(stable_variables[1:30,], aes(x=reorder(as.factor(protein),-abs(times_selected),mean), y=times_selected, col =reorder(as.factor(protein),-abs(times_selected),mean), fill =reorder(as.factor(protein),-abs(times_selected),mean))) + geom_col(show.legend = FALSE, alpha = 0.6) + theme(axis.text.x = element_text(angle = 90, hjust = 1), axis.text=element_text(size=10)) + xlab("Protein") + ylab("Times selected") 
STABVARS <- stable_variables$protein[1:30]
STABVARS.ind <- which(colnames(data) %in% STABVARS)We now have a collection of variables that the lasso regression repeatedly chose. Due to the size of the data set, instability still exists after 100 iterations, and only roughly 30 variables were chosen more frequently than 20% of the time. These are the factors that we will classify using.
An indication of how well the chosen protein variables will be able to categorize the subtypes will be provided by visualizing the relative amounts of the most-selected protein in patients with each subtype of cancer:
library(gridExtra)##
## Attaching package: 'gridExtra'## The following object is masked from 'package:dplyr':
##
## combine## The following object is masked from 'package:randomForest':
##
## combinefor (i in 1:length(STABVARS[1:5])){
print(ggplot(data, aes_string("PAM50.mRNA", STABVARS[i], col="PAM50.mRNA", fill="PAM50.mRNA")) + geom_boxplot(alpha=0.3) + ggtitle(STABVARS[i]))
}
This is promising. There are clear differences in the levels of each of the selected proteins.
Now that i have slected the proteins, I am going to create a subset of data with the selected features(predictor variables) and response variable only and stored as final_data
final_data <- data[,c(21, STABVARS.ind)]
head(final_data)dim(final_data)## [1] 80 31tail(final_data)Checking the distribution of 30 proteins - built histogram using lapply()
lapply(final_data[, 2:ncol(final_data)], hist)
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## [1] -6 -5 -4 -3 -2 -1 0 1 2 3 4
##
## $counts
## [1] 1 0 2 1 18 26 22 6 1 3
##
## $density
## [1] 0.0125 0.0000 0.0250 0.0125 0.2250 0.3250 0.2750 0.0750 0.0125 0.0375
##
## $mids
## [1] -5.5 -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5
##
## $xname
## [1] "X[[i]]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## $NP_004522
## $breaks
## [1] -5 -4 -3 -2 -1 0 1 2 3
##
## $counts
## [1] 2 0 5 17 23 19 11 3
##
## $density
## [1] 0.0250 0.0000 0.0625 0.2125 0.2875 0.2375 0.1375 0.0375
##
## $mids
## [1] -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5
##
## $xname
## [1] "X[[i]]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## $NP_003767
## $breaks
## [1] -2.0 -1.5 -1.0 -0.5 0.0 0.5 1.0 1.5 2.0 2.5 3.0
##
## $counts
## [1] 3 9 11 20 20 7 7 1 1 1
##
## $density
## [1] 0.075 0.225 0.275 0.500 0.500 0.175 0.175 0.025 0.025 0.025
##
## $mids
## [1] -1.75 -1.25 -0.75 -0.25 0.25 0.75 1.25 1.75 2.25 2.75
##
## $xname
## [1] "X[[i]]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## $NP_848597
## $breaks
## [1] -4 -3 -2 -1 0 1 2 3 4
##
## $counts
## [1] 1 2 8 22 26 11 8 2
##
## $density
## [1] 0.0125 0.0250 0.1000 0.2750 0.3250 0.1375 0.1000 0.0250
##
## $mids
## [1] -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5
##
## $xname
## [1] "X[[i]]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"
##
## $NP_005970
## $breaks
## [1] -5 -4 -3 -2 -1 0 1 2 3 4 5 6
##
## $counts
## [1] 1 5 5 12 18 14 7 9 4 3 2
##
## $density
## [1] 0.0125 0.0625 0.0625 0.1500 0.2250 0.1750 0.0875 0.1125 0.0500 0.0375
## [11] 0.0250
##
## $mids
## [1] -4.5 -3.5 -2.5 -1.5 -0.5 0.5 1.5 2.5 3.5 4.5 5.5
##
## $xname
## [1] "X[[i]]"
##
## $equidist
## [1] TRUE
##
## attr(,"class")
## [1] "histogram"Detection of outliers and data imputation
- On observing the box plot of 30 column, I got to know that it has a few outliers
- I have also created the function which can identify the presence of outliers in each varaible
- I removed these outliers and imputed them with median value
lapply(final_data[, 2:ncol(final_data)], boxplot)
## $NP_038479
## $NP_038479$stats
## [,1]
## [1,] -3.9159043
## [2,] -1.3588005
## [3,] -0.6057669
## [4,] 0.6575331
## [5,] 2.2302073
##
## $NP_038479$n
## [1] 80
##
## $NP_038479$conf
## [,1]
## [1,] -0.9619510
## [2,] -0.2495829
##
## $NP_038479$out
## [1] 3.785229
##
## $NP_038479$group
## [1] 1
##
## $NP_038479$names
## [1] "1"
##
##
## $NP_001258898
## $NP_001258898$stats
## [,1]
## [1,] -1.441904379
## [2,] -0.744144882
## [3,] 0.001291721
## [4,] 0.601259769
## [5,] 1.698927450
##
## $NP_001258898$n
## [1] 80
##
## $NP_001258898$conf
## [,1]
## [1,] -0.2363732
## [2,] 0.2389566
##
## $NP_001258898$out
## numeric(0)
##
## $NP_001258898$group
## numeric(0)
##
## $NP_001258898$names
## [1] "1"
##
##
## $NP_542785
## $NP_542785$stats
## [,1]
## [1,] -7.15625291
## [2,] -2.38215725
## [3,] 0.03094425
## [4,] 1.48903278
## [5,] 5.18818807
##
## $NP_542785$n
## [1] 80
##
## $NP_542785$conf
## [,1]
## [1,] -0.6528990
## [2,] 0.7147875
##
## $NP_542785$out
## [1] -8.643742 -8.192980
##
## $NP_542785$group
## [1] 1 1
##
## $NP_542785$names
## [1] "1"
##
##
## $NP_002005
## $NP_002005$stats
## [,1]
## [1,] -4.6253957
## [2,] -1.8720616
## [3,] -0.6925908
## [4,] 0.5003105
## [5,] 3.4240101
##
## $NP_002005$n
## [1] 80
##
## $NP_002005$conf
## [,1]
## [1,] -1.1116688
## [2,] -0.2735127
##
## $NP_002005$out
## numeric(0)
##
## $NP_002005$group
## numeric(0)
##
## $NP_002005$names
## [1] "1"
##
##
## $XP_003846537
## $XP_003846537$stats
## [,1]
## [1,] -1.3445653
## [2,] -0.4809037
## [3,] -0.2429975
## [4,] 0.4367871
## [5,] 1.5662875
##
## $XP_003846537$n
## [1] 80
##
## $XP_003846537$conf
## [,1]
## [1,] -0.40510701
## [2,] -0.08088803
##
## $XP_003846537$out
## [1] 2.336572 1.902276 2.470569 2.210577 -2.042273
##
## $XP_003846537$group
## [1] 1 1 1 1 1
##
## $XP_003846537$names
## [1] "1"
##
##
## $NP_660208
## $NP_660208$stats
## [,1]
## [1,] -1.495746821
## [2,] -0.493137518
## [3,] -0.004159959
## [4,] 0.567662746
## [5,] 1.657515110
##
## $NP_660208$n
## [1] 80
##
## $NP_660208$conf
## [,1]
## [1,] -0.1915497
## [2,] 0.1832297
##
## $NP_660208$out
## numeric(0)
##
## $NP_660208$group
## numeric(0)
##
## $NP_660208$names
## [1] "1"
##
##
## $NP_004439
## $NP_004439$stats
## [,1]
## [1,] -5.887760
## [2,] -3.844681
## [3,] -2.475620
## [4,] -1.124023
## [5,] 2.103030
##
## $NP_004439$n
## [1] 80
##
## $NP_004439$conf
## [,1]
## [1,] -2.956223
## [2,] -1.995018
##
## $NP_004439$out
## [1] 9.668177 9.438237 2.995443 3.853287 5.635589 3.886441 3.322970 5.767942
## [9] 5.800973 4.567836
##
## $NP_004439$group
## [1] 1 1 1 1 1 1 1 1 1 1
##
## $NP_004439$names
## [1] "1"
##
##
## $NP_004388
## $NP_004388$stats
## [,1]
## [1,] -1.4793025
## [2,] -0.4315964
## [3,] -0.0896896
## [4,] 0.4370189
## [5,] 1.7005950
##
## $NP_004388$n
## [1] 80
##
## $NP_004388$conf
## [,1]
## [1,] -0.24312994
## [2,] 0.06375073
##
## $NP_004388$out
## [1] 2.286965
##
## $NP_004388$group
## [1] 1
##
## $NP_004388$names
## [1] "1"
##
##
## $NP_001229372
## $NP_001229372$stats
## [,1]
## [1,] -6.963267
## [2,] -3.590168
## [3,] -2.435468
## [4,] -1.275306
## [5,] 2.133122
##
## $NP_001229372$n
## [1] 80
##
## $NP_001229372$conf
## [,1]
## [1,] -2.844387
## [2,] -2.026549
##
## $NP_001229372$out
## [1] 8.978604 8.965470 6.148162 3.646817 3.369828 3.398403 4.232799
##
## $NP_001229372$group
## [1] 1 1 1 1 1 1 1
##
## $NP_001229372$names
## [1] "1"
##
##
## $NP_005130
## $NP_005130$stats
## [,1]
## [1,] -8.8307556
## [2,] -3.7147704
## [3,] -1.4701347
## [4,] 0.2255714
## [5,] 5.6405463
##
## $NP_005130$n
## [1] 80
##
## $NP_005130$conf
## [,1]
## [1,] -2.1661936
## [2,] -0.7740758
##
## $NP_005130$out
## [1] 6.397391
##
## $NP_005130$group
## [1] 1
##
## $NP_005130$names
## [1] "1"
##
##
## $NP_004243
## $NP_004243$stats
## [,1]
## [1,] -4.3493525
## [2,] -2.2404093
## [3,] -1.2588643
## [4,] 0.3011927
## [5,] 4.0708722
##
## $NP_004243$n
## [1] 80
##
## $NP_004243$conf
## [,1]
## [1,] -1.7078367
## [2,] -0.8098919
##
## $NP_004243$out
## [1] 4.399083 4.861847 5.671446
##
## $NP_004243$group
## [1] 1 1 1
##
## $NP_004243$names
## [1] "1"
##
##
## $NP_003144
## $NP_003144$stats
## [,1]
## [1,] -1.9798402
## [2,] -0.4325426
## [3,] 0.1947012
## [4,] 0.7404052
## [5,] 1.8449029
##
## $NP_003144$n
## [1] 80
##
## $NP_003144$conf
## [,1]
## [1,] -0.01249928
## [2,] 0.40190170
##
## $NP_003144$out
## [1] 2.830061
##
## $NP_003144$group
## [1] 1
##
## $NP_003144$names
## [1] "1"
##
##
## $NP_001171551
## $NP_001171551$stats
## [,1]
## [1,] -1.9470415
## [2,] -0.4322866
## [3,] 0.1742170
## [4,] 0.7294963
## [5,] 1.8449029
##
## $NP_001171551$n
## [1] 80
##
## $NP_001171551$conf
## [,1]
## [1,] -0.03101126
## [2,] 0.37944520
##
## $NP_001171551$out
## [1] 2.813958
##
## $NP_001171551$group
## [1] 1
##
## $NP_001171551$names
## [1] "1"
##
##
## $NP_072096
## $NP_072096$stats
## [,1]
## [1,] -1.50276929
## [2,] -0.55484927
## [3,] -0.01969573
## [4,] 0.41592377
## [5,] 1.19362341
##
## $NP_072096$n
## [1] 80
##
## $NP_072096$conf
## [,1]
## [1,] -0.1911822
## [2,] 0.1517907
##
## $NP_072096$out
## [1] -2.871865
##
## $NP_072096$group
## [1] 1
##
## $NP_072096$names
## [1] "1"
##
##
## $NP_005484
## $NP_005484$stats
## [,1]
## [1,] -1.50816552
## [2,] -0.53675374
## [3,] -0.09190035
## [4,] 0.39283208
## [5,] 1.56245999
##
## $NP_005484$n
## [1] 80
##
## $NP_005484$conf
## [,1]
## [1,] -0.25611110
## [2,] 0.07231039
##
## $NP_005484$out
## numeric(0)
##
## $NP_005484$group
## numeric(0)
##
## $NP_005484$names
## [1] "1"
##
##
## $NP_061170
## $NP_061170$stats
## [,1]
## [1,] -4.712975
## [2,] -2.025168
## [3,] -1.033795
## [4,] 0.311528
## [5,] 3.724644
##
## $NP_061170$n
## [1] 80
##
## $NP_061170$conf
## [,1]
## [1,] -1.4465707
## [2,] -0.6210191
##
## $NP_061170$out
## [1] 4.029373 5.689747
##
## $NP_061170$group
## [1] 1 1
##
## $NP_061170$names
## [1] "1"
##
##
## $NP_653304
## $NP_653304$stats
## [,1]
## [1,] -4.2030116
## [2,] -1.9997588
## [3,] -0.8129121
## [4,] 0.6002230
## [5,] 2.5447948
##
## $NP_653304$n
## [1] 80
##
## $NP_653304$conf
## [,1]
## [1,] -1.2721972
## [2,] -0.3536269
##
## $NP_653304$out
## numeric(0)
##
## $NP_653304$group
## numeric(0)
##
## $NP_653304$names
## [1] "1"
##
##
## $NP_653081
## $NP_653081$stats
## [,1]
## [1,] -1.8526870
## [2,] -0.5220216
## [3,] -0.1564469
## [4,] 0.4245559
## [5,] 1.6049629
##
## $NP_653081$n
## [1] 80
##
## $NP_653081$conf
## [,1]
## [1,] -0.32365917
## [2,] 0.01076545
##
## $NP_653081$out
## [1] 2.147486 1.989637 3.624280 1.938567
##
## $NP_653081$group
## [1] 1 1 1 1
##
## $NP_653081$names
## [1] "1"
##
##
## $NP_057164
## $NP_057164$stats
## [,1]
## [1,] -1.92397415
## [2,] -0.71653848
## [3,] -0.03968425
## [4,] 1.06566335
## [5,] 3.01239495
##
## $NP_057164$n
## [1] 80
##
## $NP_057164$conf
## [,1]
## [1,] -0.3545091
## [2,] 0.2751406
##
## $NP_057164$out
## numeric(0)
##
## $NP_057164$group
## numeric(0)
##
## $NP_057164$names
## [1] "1"
##
##
## $NP_004453
## $NP_004453$stats
## [,1]
## [1,] -4.1386672
## [2,] -1.5225765
## [3,] -0.3784592
## [4,] 0.4924532
## [5,] 3.3454346
##
## $NP_004453$n
## [1] 80
##
## $NP_004453$conf
## [,1]
## [1,] -0.73441287
## [2,] -0.02250543
##
## $NP_004453$out
## [1] 3.686225 3.837949 4.243675
##
## $NP_004453$group
## [1] 1 1 1
##
## $NP_004453$names
## [1] "1"
##
##
## $NP_003212
## $NP_003212$stats
## [,1]
## [1,] -3.4500588
## [2,] -1.0539582
## [3,] -0.3422749
## [4,] 0.6543596
## [5,] 2.9986201
##
## $NP_003212$n
## [1] 80
##
## $NP_003212$conf
## [,1]
## [1,] -0.64404820
## [2,] -0.04050166
##
## $NP_003212$out
## [1] -4.1699
##
## $NP_003212$group
## [1] 1
##
## $NP_003212$names
## [1] "1"
##
##
## $NP_060866
## $NP_060866$stats
## [,1]
## [1,] -1.9687931
## [2,] -0.7490088
## [3,] -0.3001057
## [4,] 0.2604499
## [5,] 1.2134275
##
## $NP_060866$n
## [1] 80
##
## $NP_060866$conf
## [,1]
## [1,] -0.4784260
## [2,] -0.1217855
##
## $NP_060866$out
## [1] -2.589974 2.460358 1.867033 2.926207
##
## $NP_060866$group
## [1] 1 1 1 1
##
## $NP_060866$names
## [1] "1"
##
##
## $NP_001160163
## $NP_001160163$stats
## [,1]
## [1,] -3.42210803
## [2,] -1.01917050
## [3,] 0.06316141
## [4,] 0.73348041
## [5,] 2.89217103
##
## $NP_001160163$n
## [1] 80
##
## $NP_001160163$conf
## [,1]
## [1,] -0.2464433
## [2,] 0.3727661
##
## $NP_001160163$out
## numeric(0)
##
## $NP_001160163$group
## numeric(0)
##
## $NP_001160163$names
## [1] "1"
##
##
## $NP_003875
## $NP_003875$stats
## [,1]
## [1,] -3.39803927
## [2,] -0.86429123
## [3,] 0.04810062
## [4,] 0.88773048
## [5,] 3.29279523
##
## $NP_003875$n
## [1] 80
##
## $NP_003875$conf
## [,1]
## [1,] -0.2613929
## [2,] 0.3575941
##
## $NP_003875$out
## [1] -4.970156 5.432549 11.755122 -4.153530 -4.176455
##
## $NP_003875$group
## [1] 1 1 1 1 1
##
## $NP_003875$names
## [1] "1"
##
##
## $NP_002677
## $NP_002677$stats
## [,1]
## [1,] -11.361115
## [2,] -8.205643
## [3,] -6.362650
## [4,] -4.197724
## [5,] 1.516256
##
## $NP_002677$n
## [1] 80
##
## $NP_002677$conf
## [,1]
## [1,] -7.070647
## [2,] -5.654654
##
## $NP_002677$out
## [1] 4.331248 1.864520 5.868250 10.215922 5.871759
##
## $NP_002677$group
## [1] 1 1 1 1 1
##
## $NP_002677$names
## [1] "1"
##
##
## $NP_001135891
## $NP_001135891$stats
## [,1]
## [1,] -3.1680635
## [2,] -1.0661463
## [3,] -0.2271340
## [4,] 0.5092675
## [5,] 2.5193274
##
## $NP_001135891$n
## [1] 80
##
## $NP_001135891$conf
## [,1]
## [1,] -0.50542981
## [2,] 0.05116191
##
## $NP_001135891$out
## [1] -5.050298 3.201698 3.175491 3.414158
##
## $NP_001135891$group
## [1] 1 1 1 1
##
## $NP_001135891$names
## [1] "1"
##
##
## $NP_004522
## $NP_004522$stats
## [,1]
## [1,] -2.7021650
## [2,] -1.1902918
## [3,] -0.2807051
## [4,] 0.6040038
## [5,] 2.8653784
##
## $NP_004522$n
## [1] 80
##
## $NP_004522$conf
## [,1]
## [1,] -0.59766633
## [2,] 0.03625606
##
## $NP_004522$out
## [1] -4.150392 -4.996855
##
## $NP_004522$group
## [1] 1 1
##
## $NP_004522$names
## [1] "1"
##
##
## $NP_003767
## $NP_003767$stats
## [,1]
## [1,] -1.87517546
## [2,] -0.55882109
## [3,] -0.06146679
## [4,] 0.46892980
## [5,] 1.60169464
##
## $NP_003767$n
## [1] 80
##
## $NP_003767$conf
## [,1]
## [1,] -0.2430183
## [2,] 0.1200848
##
## $NP_003767$out
## [1] 2.142263 2.677884
##
## $NP_003767$group
## [1] 1 1
##
## $NP_003767$names
## [1] "1"
##
##
## $NP_848597
## $NP_848597$stats
## [,1]
## [1,] -2.8023343
## [2,] -0.6238947
## [3,] 0.2488007
## [4,] 1.1953659
## [5,] 3.2187065
##
## $NP_848597$n
## [1] 80
##
## $NP_848597$conf
## [,1]
## [1,] -0.07257056
## [2,] 0.57017190
##
## $NP_848597$out
## [1] -3.986077
##
## $NP_848597$group
## [1] 1
##
## $NP_848597$names
## [1] "1"
##
##
## $NP_005970
## $NP_005970$stats
## [,1]
## [1,] -4.18213251
## [2,] -1.24546089
## [3,] -0.05750858
## [4,] 1.65100594
## [5,] 5.16274745
##
## $NP_005970$n
## [1] 80
##
## $NP_005970$conf
## [,1]
## [1,] -0.5691676
## [2,] 0.4541505
##
## $NP_005970$out
## numeric(0)
##
## $NP_005970$group
## numeric(0)
##
## $NP_005970$names
## [1] "1"# function for detection of outliers in each column
outliers <- function(x)
{
for(i in 1:ncol(x))
{
sd_i <- sd(x[,i])
mean_i <- mean(x[,i])
out = x[x[,i] > 3*sd_i+mean_i | x[,i] < mean_i-3*sd_i, ]
if(nrow(out) > 0)
{
print(colnames(x)[i])
paste("The outliers are -", out)
}else
{
print(paste("No outliers for",colnames(x)[i]))
}
}
}# Detecting outliers in the dataset
outliers(final_data[,c(2:ncol(final_data))])## [1] "NP_038479"
## [1] "No outliers for NP_001258898"
## [1] "No outliers for NP_542785"
## [1] "No outliers for NP_002005"
## [1] "No outliers for XP_003846537"
## [1] "No outliers for NP_660208"
## [1] "NP_004439"
## [1] "No outliers for NP_004388"
## [1] "NP_001229372"
## [1] "No outliers for NP_005130"
## [1] "No outliers for NP_004243"
## [1] "NP_003144"
## [1] "NP_001171551"
## [1] "NP_072096"
## [1] "No outliers for NP_005484"
## [1] "No outliers for NP_061170"
## [1] "No outliers for NP_653304"
## [1] "NP_653081"
## [1] "No outliers for NP_057164"
## [1] "No outliers for NP_004453"
## [1] "No outliers for NP_003212"
## [1] "NP_060866"
## [1] "No outliers for NP_001160163"
## [1] "NP_003875"
## [1] "NP_002677"
## [1] "NP_001135891"
## [1] "NP_004522"
## [1] "NP_003767"
## [1] "NP_848597"
## [1] "No outliers for NP_005970"# replacing outliers with median imputation
outlier <- function(x) {
x[x < quantile(x,0.25) - 1.5 * IQR(x) | x > quantile(x,0.75) + 1.5 * IQR(x)] <- median(x)
x
}data_out <- as.data.frame(lapply(final_data[,c(2:ncol(final_data))], outlier))
data_norm <- data_out
data_norm$PAM50.mRNA <- final_data$PAM50.mRNAFeature Engineering -
I have not derived new features. - converted the PAM50.mRNA as a factor type as this is a response variable. for this I have used factor()
# changing the names
data_norm$PAM50.mRNA[which(data_norm$PAM50.mRNA == "Basal-like")] = "Basal.like"
data_norm$PAM50.mRNA[which(data_norm$PAM50.mRNA == "HER2-enriched")] = "HER2.enriched"
data_norm$PAM50.mRNA[which(data_norm$PAM50.mRNA == "Luminal A")] = "Luminal.A"
data_norm$PAM50.mRNA[which(data_norm$PAM50.mRNA == "Luminal B")] = "Luminal.B"# converting PAM50.mRNA to a factor type
data_norm$PAM50.mRNA <- factor(data_norm$PAM50.mRNA)Correlation/Collinearity analysis
- Numerical data is required for calculating correlation, so I have used only numerical variables to interpret the correlation
- Correlation plot is shown for whole data
- I have shown the plot of correlation between numeric features
- I also tried pairs.panels function for correlation but since there are more than 15 features. Plots are not clearly visible pairs.panels(data_n)
- Thefore, build a plot with top ten features
- I cannot apply chi-square test as the datapoints should be non-zero and non-negative
#Creating a correlation plot of whole dataset
cormat <- round(cor(data_norm[,1:30]),2)
cormat## NP_038479 NP_001258898 NP_542785 NP_002005 XP_003846537 NP_660208
## NP_038479 1.00 -0.41 0.20 -0.14 0.33 -0.05
## NP_001258898 -0.41 1.00 -0.04 0.33 -0.30 -0.13
## NP_542785 0.20 -0.04 1.00 -0.34 0.07 -0.35
## NP_002005 -0.14 0.33 -0.34 1.00 0.09 0.18
## XP_003846537 0.33 -0.30 0.07 0.09 1.00 0.06
## NP_660208 -0.05 -0.13 -0.35 0.18 0.06 1.00
## NP_004439 0.13 0.05 0.07 0.06 -0.05 -0.03
## NP_004388 -0.45 0.56 0.07 0.03 -0.22 -0.10
## NP_001229372 0.09 0.06 0.13 0.00 0.00 -0.07
## NP_005130 0.05 -0.04 0.42 -0.49 -0.09 -0.18
## NP_004243 0.01 0.12 -0.39 0.64 0.17 0.24
## NP_003144 0.36 -0.51 0.16 -0.24 0.19 0.13
## NP_001171551 0.35 -0.53 0.16 -0.25 0.19 0.14
## NP_072096 0.12 -0.16 -0.29 0.07 -0.04 0.52
## NP_005484 -0.36 0.34 -0.15 0.02 -0.18 -0.08
## NP_061170 -0.01 -0.04 0.06 0.01 0.17 -0.08
## NP_653304 -0.25 0.53 0.09 0.17 -0.05 -0.21
## NP_653081 0.05 0.18 0.15 0.09 0.02 -0.28
## NP_057164 0.11 -0.51 -0.01 0.06 0.26 0.35
## NP_004453 -0.13 0.32 0.07 0.07 -0.19 -0.25
## NP_003212 -0.22 0.23 0.23 -0.08 -0.05 -0.38
## NP_060866 -0.02 0.18 -0.32 0.32 -0.01 0.28
## NP_001160163 -0.40 0.46 -0.03 -0.11 -0.37 -0.05
## NP_003875 0.34 -0.30 -0.18 -0.01 0.15 0.09
## NP_002677 -0.04 0.09 -0.03 0.02 -0.14 0.13
## NP_001135891 -0.03 0.18 -0.17 0.34 0.04 -0.04
## NP_004522 0.00 -0.08 -0.33 0.27 0.08 0.26
## NP_003767 -0.19 0.06 -0.30 0.36 -0.11 0.25
## NP_848597 0.38 -0.36 -0.03 0.18 0.26 0.23
## NP_005970 0.27 -0.41 0.10 -0.15 0.21 0.28
## NP_004439 NP_004388 NP_001229372 NP_005130 NP_004243 NP_003144
## NP_038479 0.13 -0.45 0.09 0.05 0.01 0.36
## NP_001258898 0.05 0.56 0.06 -0.04 0.12 -0.51
## NP_542785 0.07 0.07 0.13 0.42 -0.39 0.16
## NP_002005 0.06 0.03 0.00 -0.49 0.64 -0.24
## XP_003846537 -0.05 -0.22 0.00 -0.09 0.17 0.19
## NP_660208 -0.03 -0.10 -0.07 -0.18 0.24 0.13
## NP_004439 1.00 0.06 0.78 0.01 0.05 -0.04
## NP_004388 0.06 1.00 0.19 0.16 -0.09 -0.48
## NP_001229372 0.78 0.19 1.00 0.09 -0.04 -0.04
## NP_005130 0.01 0.16 0.09 1.00 -0.49 0.05
## NP_004243 0.05 -0.09 -0.04 -0.49 1.00 -0.04
## NP_003144 -0.04 -0.48 -0.04 0.05 -0.04 1.00
## NP_001171551 -0.05 -0.48 -0.05 0.05 -0.04 1.00
## NP_072096 -0.01 -0.17 -0.10 -0.10 0.15 0.17
## NP_005484 0.13 0.30 0.17 0.02 0.02 -0.37
## NP_061170 0.06 -0.02 0.13 -0.17 0.13 -0.10
## NP_653304 0.15 0.67 0.22 0.02 0.10 -0.58
## NP_653081 0.05 0.25 0.16 0.22 0.14 -0.23
## NP_057164 -0.13 -0.36 -0.17 -0.01 0.15 0.49
## NP_004453 0.17 0.22 0.29 -0.05 0.06 -0.28
## NP_003212 0.10 0.28 0.13 0.20 -0.15 -0.17
## NP_060866 0.03 0.12 0.07 -0.28 0.37 -0.17
## NP_001160163 0.01 0.38 0.07 0.11 -0.20 -0.33
## NP_003875 -0.13 -0.21 -0.13 -0.13 0.11 0.23
## NP_002677 0.38 0.23 0.41 0.12 0.03 -0.02
## NP_001135891 -0.13 0.00 -0.14 -0.16 0.27 -0.16
## NP_004522 0.01 -0.16 -0.12 -0.21 0.38 0.14
## NP_003767 0.23 0.07 0.15 -0.27 0.41 -0.24
## NP_848597 0.10 -0.26 0.13 -0.05 0.23 0.20
## NP_005970 -0.16 -0.20 -0.10 0.14 0.00 0.47
## NP_001171551 NP_072096 NP_005484 NP_061170 NP_653304 NP_653081
## NP_038479 0.35 0.12 -0.36 -0.01 -0.25 0.05
## NP_001258898 -0.53 -0.16 0.34 -0.04 0.53 0.18
## NP_542785 0.16 -0.29 -0.15 0.06 0.09 0.15
## NP_002005 -0.25 0.07 0.02 0.01 0.17 0.09
## XP_003846537 0.19 -0.04 -0.18 0.17 -0.05 0.02
## NP_660208 0.14 0.52 -0.08 -0.08 -0.21 -0.28
## NP_004439 -0.05 -0.01 0.13 0.06 0.15 0.05
## NP_004388 -0.48 -0.17 0.30 -0.02 0.67 0.25
## NP_001229372 -0.05 -0.10 0.17 0.13 0.22 0.16
## NP_005130 0.05 -0.10 0.02 -0.17 0.02 0.22
## NP_004243 -0.04 0.15 0.02 0.13 0.10 0.14
## NP_003144 1.00 0.17 -0.37 -0.10 -0.58 -0.23
## NP_001171551 1.00 0.18 -0.37 -0.08 -0.59 -0.23
## NP_072096 0.18 1.00 -0.14 -0.18 -0.29 -0.19
## NP_005484 -0.37 -0.14 1.00 0.18 0.26 0.04
## NP_061170 -0.08 -0.18 0.18 1.00 0.13 0.18
## NP_653304 -0.59 -0.29 0.26 0.13 1.00 0.40
## NP_653081 -0.23 -0.19 0.04 0.18 0.40 1.00
## NP_057164 0.50 0.31 -0.33 -0.13 -0.50 -0.19
## NP_004453 -0.29 -0.25 0.36 0.25 0.24 0.22
## NP_003212 -0.18 -0.32 0.24 -0.01 0.23 0.16
## NP_060866 -0.18 0.11 0.14 0.14 0.16 0.04
## NP_001160163 -0.32 0.03 0.33 -0.02 0.30 0.14
## NP_003875 0.22 0.06 -0.30 -0.15 -0.18 -0.08
## NP_002677 0.00 0.10 0.23 0.04 0.06 0.18
## NP_001135891 -0.16 -0.03 -0.06 0.01 0.13 0.14
## NP_004522 0.15 0.13 0.18 0.14 -0.23 -0.08
## NP_003767 -0.21 0.07 -0.08 0.14 0.18 0.09
## NP_848597 0.22 0.27 -0.28 -0.15 -0.23 0.10
## NP_005970 0.48 0.28 -0.22 -0.16 -0.43 -0.11
## NP_057164 NP_004453 NP_003212 NP_060866 NP_001160163 NP_003875
## NP_038479 0.11 -0.13 -0.22 -0.02 -0.40 0.34
## NP_001258898 -0.51 0.32 0.23 0.18 0.46 -0.30
## NP_542785 -0.01 0.07 0.23 -0.32 -0.03 -0.18
## NP_002005 0.06 0.07 -0.08 0.32 -0.11 -0.01
## XP_003846537 0.26 -0.19 -0.05 -0.01 -0.37 0.15
## NP_660208 0.35 -0.25 -0.38 0.28 -0.05 0.09
## NP_004439 -0.13 0.17 0.10 0.03 0.01 -0.13
## NP_004388 -0.36 0.22 0.28 0.12 0.38 -0.21
## NP_001229372 -0.17 0.29 0.13 0.07 0.07 -0.13
## NP_005130 -0.01 -0.05 0.20 -0.28 0.11 -0.13
## NP_004243 0.15 0.06 -0.15 0.37 -0.20 0.11
## NP_003144 0.49 -0.28 -0.17 -0.17 -0.33 0.23
## NP_001171551 0.50 -0.29 -0.18 -0.18 -0.32 0.22
## NP_072096 0.31 -0.25 -0.32 0.11 0.03 0.06
## NP_005484 -0.33 0.36 0.24 0.14 0.33 -0.30
## NP_061170 -0.13 0.25 -0.01 0.14 -0.02 -0.15
## NP_653304 -0.50 0.24 0.23 0.16 0.30 -0.18
## NP_653081 -0.19 0.22 0.16 0.04 0.14 -0.08
## NP_057164 1.00 -0.47 -0.24 -0.16 -0.37 0.14
## NP_004453 -0.47 1.00 -0.01 0.20 0.26 -0.25
## NP_003212 -0.24 -0.01 1.00 -0.14 -0.08 -0.16
## NP_060866 -0.16 0.20 -0.14 1.00 -0.01 0.14
## NP_001160163 -0.37 0.26 -0.08 -0.01 1.00 -0.38
## NP_003875 0.14 -0.25 -0.16 0.14 -0.38 1.00
## NP_002677 -0.06 0.20 0.07 0.06 0.20 -0.12
## NP_001135891 -0.09 0.03 -0.09 0.22 0.19 -0.04
## NP_004522 0.22 0.02 -0.19 0.20 -0.07 -0.01
## NP_003767 0.04 0.08 -0.17 0.23 -0.05 0.01
## NP_848597 0.36 -0.29 -0.17 0.06 -0.35 0.20
## NP_005970 0.56 -0.45 -0.21 -0.15 -0.20 0.25
## NP_002677 NP_001135891 NP_004522 NP_003767 NP_848597 NP_005970
## NP_038479 -0.04 -0.03 0.00 -0.19 0.38 0.27
## NP_001258898 0.09 0.18 -0.08 0.06 -0.36 -0.41
## NP_542785 -0.03 -0.17 -0.33 -0.30 -0.03 0.10
## NP_002005 0.02 0.34 0.27 0.36 0.18 -0.15
## XP_003846537 -0.14 0.04 0.08 -0.11 0.26 0.21
## NP_660208 0.13 -0.04 0.26 0.25 0.23 0.28
## NP_004439 0.38 -0.13 0.01 0.23 0.10 -0.16
## NP_004388 0.23 0.00 -0.16 0.07 -0.26 -0.20
## NP_001229372 0.41 -0.14 -0.12 0.15 0.13 -0.10
## NP_005130 0.12 -0.16 -0.21 -0.27 -0.05 0.14
## NP_004243 0.03 0.27 0.38 0.41 0.23 0.00
## NP_003144 -0.02 -0.16 0.14 -0.24 0.20 0.47
## NP_001171551 0.00 -0.16 0.15 -0.21 0.22 0.48
## NP_072096 0.10 -0.03 0.13 0.07 0.27 0.28
## NP_005484 0.23 -0.06 0.18 -0.08 -0.28 -0.22
## NP_061170 0.04 0.01 0.14 0.14 -0.15 -0.16
## NP_653304 0.06 0.13 -0.23 0.18 -0.23 -0.43
## NP_653081 0.18 0.14 -0.08 0.09 0.10 -0.11
## NP_057164 -0.06 -0.09 0.22 0.04 0.36 0.56
## NP_004453 0.20 0.03 0.02 0.08 -0.29 -0.45
## NP_003212 0.07 -0.09 -0.19 -0.17 -0.17 -0.21
## NP_060866 0.06 0.22 0.20 0.23 0.06 -0.15
## NP_001160163 0.20 0.19 -0.07 -0.05 -0.35 -0.20
## NP_003875 -0.12 -0.04 -0.01 0.01 0.20 0.25
## NP_002677 1.00 0.09 0.02 0.05 0.04 -0.01
## NP_001135891 0.09 1.00 0.19 0.21 -0.05 -0.20
## NP_004522 0.02 0.19 1.00 0.19 0.19 0.02
## NP_003767 0.05 0.21 0.19 1.00 0.12 -0.10
## NP_848597 0.04 -0.05 0.19 0.12 1.00 0.43
## NP_005970 -0.01 -0.20 0.02 -0.10 0.43 1.00We can say that the proteins are not highly correlated
melted_cormat <- reshape2::melt(cormat)Visualizing the correlation plot
ggplot(data = melted_cormat, aes(Var2, Var1, fill = value))+
geom_tile(color = "white")+
scale_fill_gradient2(low = "blue", high = "red", mid = "white",
midpoint = 0, limit = c(-1,1), space = "Lab",
name="Pearson\nCorrelation") +
theme_minimal()+
theme(axis.text.x = element_text(angle = 45, vjust = 1,
size = 12, hjust = 1))+
coord_fixed()+xlab("")+ylab("")+ggtitle("Correlation plot")
Top 5 variables and response variable
top_variables <- c("PAM50.mRNA", "NP_660208", "NP_005970", "NP_004453", "NP_004522", "NP_001258898")
#"NP_001160163", "NP_003767", "NP_542785", "NP_060866", "NP_004388"
data_viz= data_norm[,(names(data_norm) %in% top_variables)]pairs(data_viz)
pairs.panels(data_viz)
ggpairs(data_viz)## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.
The variable names are displayed on the outer edges of the matrix. The boxes along the diagonals display the density plot for each variable. The boxes in the lower left corner display the scatterplot between each variable. The boxes in the upper right corner display the Pearson correlation coefficient between each variable. The variables are not correlated but according to the distribution they have normal distribution
- Model Construction & Evaluation
• which algorithms will you use and why? naive bayes, knn, decision trees, rules, log regression, multi regression, lasso, ridge, neural net, svm, clustering
• is the algorithm compatible with the features you have in the data set?
As this is a multi-class classification, I am going to use SVM, Neural Networks, Decison tree and Naive Baye’s. 1. SVM : I chose this algorithm as it does complex data transformations depending on the selected kernel function and based on that transformations, it tries to maximize the separation boundaries between the data points depending on the labels or classes defined. SVM works well for binary classification. But, For multi-class classification, the same principle for binary classfication is utilized after breaking down the multiclassification problem into multiple binary classification problems. The main reason behind choosing this alogirthm is it works well with expression data. As I am dealing with the protein expression data, this algorithm will work well and compatiable with the features in the data-set. The kernel function is used where the complexity of the problem is high and data is linearly separable. where it adds multiple polynomial features at a very high degree this way it prevents the computational complexity or burden that comes along adding multiple features to data. The dependencies of the variable are always taken into consideration
Neural Networks : I chose this algorithm as this works well with complex datset. The output layer contains one neuron per class rather than just one neuron. If the dataset contains four classes, then the output layer has four neurons. Therefore, it works well with multi class classfication problems.
Random Forests : I chose this algorithm as this a classifier is a systematic approach for multiclass classification. It poses a set of questions to the dataset (related to its attributes/features). The benefit of this method is that it can handle missing values and maintains accuracy for missing data. It won’t over fit the model. t handles dataset with higher dimensionality
Naive bayes : I chose naive bayes algorithm as this is highly compatiable for protein expression datset and the calculations of the probabilities for each class are simplified to make their calculations tractable.
• how do you compare and evaluate the performance of the algorithms? R-Squared? MAD, MSE, RMSE? AIC? AUC? why are they that way?
As this is a classification problem, Classification Metrics are :Confusion Matrix, Precision, Recall, F1-score and AUC I chose these metrics as it is a well-balanced datasetand these metrics works well.
• how do you choose training vs validation data? why? Data splitting is done in 70:20 ratio. Partition is created using createDataPartition function. As it is a well balanced datset.
• can you and should you use k-fold cross-validation? - k-Fold Cross Validation is done for the whole dataset - I have used k = 10 which means 10 folds take place along with 10 repetitions - For testing the data, I have used 3 models to test the k-fold CV
• how would you build a stacked ensemble model? is it a better model? can you use boosting or bagging? or build a stacked learner?
• how will you communicate the results of your algorithms? I have used confusionMatrix function to interpret the results of the algorithm
Creation of training & validation subsets
- Data splitting is done in 70:20 ratio
- Partition is created using createDataPartition function
Evaluation of fit of models with holdout method
- For model evaluation,I have calculated accuracy of each model using the confusionMatrix function. I have also compared the sensitivity, specificity, precision, recall and AUC of classification models
For a balanced dataset, we use a confusion matrix and the derived performance metrics; accuracy, precision, recall, F1-score and AUC
#creating test/train split index
set.seed(1000)
samp <- createDataPartition(data_norm$PAM50.mRNA, p = 0.7, list = FALSE)
train <- data_norm[samp, ]
test<- data_norm[-samp, ]#exploring training dataset
head(train)dim(train)## [1] 59 31head(test)dim(test)## [1] 21 31Model building
As this is a multi-class classification, I am implement 4 models - SVM, Neural Networks, Naive Baye’s and Random forests
SVM linear classifier
Support Vector Machines are generalized extension of a maximal margin classifier, SVM are intended for the binary classification setting when there are two classes. However, this designed intention does not disqualify from using the SVM method with cases of more than two classes. SVM determines the best line separator by identifying closest points in Convex hull, a hyperplane bisects the closest point to the convex hull. The support vector classifies a test observation depending on which side of a plane it lies; this is based on boundaries-support vectors. SVM method allows some observations to be on the incorrect side of the margin and in some cases the incorrect side of the hyperplane in the interest of performing better in classifying the remaining observations further away from the hydroplane. This is known as a soft margin classifier; training observations can violate this area. Advantages of using a SVM model are; can be adapted to work well with nonlinear boundaries, uses kernels, less overfitting of data, performs well with clear margin of separation among data. The kernel function is used where the complexity of the problem is high and data is linearly separable. where it adds multiple polynomial features at a very high degree this way it prevents the computational complexity or burden that comes along adding multiple features to data. The dependencies of the variable are always taken into consideration
svm_model1 <- svm(PAM50.mRNA~., data= train, type="C-classification", kernel = 'linear')
svm_model1##
## Call:
## svm(formula = PAM50.mRNA ~ ., data = train, type = "C-classification",
## kernel = "linear")
##
##
## Parameters:
## SVM-Type: C-classification
## SVM-Kernel: linear
## cost: 1
##
## Number of Support Vectors: 41svm_pred <- predict(svm_model1, newdata = test)
confusionMatrix(svm_pred, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 5 0 0 2
## HER2.enriched 0 2 0 0
## Luminal.A 0 0 6 1
## Luminal.B 0 1 0 4
##
## Overall Statistics
##
## Accuracy : 0.8095
## 95% CI : (0.5809, 0.9455)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 1.026e-05
##
## Kappa : 0.7399
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 1.0000 0.66667 1.0000
## Specificity 0.8750 1.00000 0.9333
## Pos Pred Value 0.7143 1.00000 0.8571
## Neg Pred Value 1.0000 0.94737 1.0000
## Precision 0.7143 1.00000 0.8571
## Recall 1.0000 0.66667 1.0000
## F1 0.8333 0.80000 0.9231
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.2381 0.09524 0.2857
## Detection Prevalence 0.3333 0.09524 0.3333
## Balanced Accuracy 0.9375 0.83333 0.9667
## Class: Luminal.B
## Sensitivity 0.5714
## Specificity 0.9286
## Pos Pred Value 0.8000
## Neg Pred Value 0.8125
## Precision 0.8000
## Recall 0.5714
## F1 0.6667
## Prevalence 0.3333
## Detection Rate 0.1905
## Detection Prevalence 0.2381
## Balanced Accuracy 0.7500accuracy_svm <-confusionMatrix(test$PAM50.mRNA, svm_pred)$overall["Accuracy"]Model evaluation for SVM:
Accuracy : The overall model accuracy of SVM model is 81%
Precision,Recall,F1 : The Precision,Recall,F1 for Basal.like class is 0.7143, 1.0000, 0.8333 The Precision,Recall,F1 for HER2.enriched class is 1.00000, 0.33333 , 0.80000
The Precision,Recall,F1 for Luminal.A class is 0.8571, 1.0000, 0.9231 The Precision,Recall,F1 for Luminal.B class is 0.8000, 0.5714, 0.6667
Sensitivity and Specificity: The Sensitivity and Specificity for Basal.like class is 1.0000 and 0.8750 The Sensitivity and Specificity for HER2.enriched class is 0.66667 and 1.00000 The Sensitivity and Specificity for Luminal.A is 1.0000 and 0.9333 The Sensitivity and Specificity for Luminal.B class is 0.5714 and 0.9286
The Kappa statistic: The kappa value for this model is 0.7399 which states that it is a good agreement.
Macro-averaged Metrics : The per-class metrics can be averaged over all the classes resulting in macro-averaged precision, recall and F-1.
# macro-averaged precision
precision_svm <- c(0.7143, 1.00000,0.8571, 0.8000)
macro_precision_svm <- mean(precision_svm)
# macro-averaged recall
recall_svm <- c(1.0000, 0.66667, 1.0000, 0.5714)
macro_recall_svm <- mean(recall_svm)
# macro-averaged F-1
F1_svm<- c(0.8333,0.80000, 0.9231, 0.6667)
macroF1_svm <- mean(F1_svm)
macro_avg_svm <- data.frame( macro_precision_svm, macro_recall_svm, macroF1_svm)
macro_avg_svmAUC for SVM
svm_auc <- multiclass.roc(test$PAM50.mRNA, as.ordered(svm_pred))
auc(svm_auc)## Multi-class area under the curve: 0.7897The AUC of SVM model is : 0.7897
Name_metrics <- c("Accuracy", "Precision", "Recall", "F-1", "AUC", "Kappa")
values_svm <- c(0.8095 , 0.84285, 0.8095175, 0.805775,0.7897, 0.7399 )
metrics_svm <- data.frame(Name_metrics, values_svm)
print (metrics_svm)## Name_metrics values_svm
## 1 Accuracy 0.8095000
## 2 Precision 0.8428500
## 3 Recall 0.8095175
## 4 F-1 0.8057750
## 5 AUC 0.7897000
## 6 Kappa 0.7399000Neural Networks
Inspired by biological neural networks, the Neural Network method is a supervised machine learning algorithm which consists of units arranged in layers which coverts an input vector (independent variable) into a prediction/classification. “The algorithm learns a function by training on a dataset without prior knowledge about the dataset.”
# I have tried out many hidden layers but got optimal results with hidden layer = 5
neuralnet_model2 <- train(PAM50.mRNA~., data= train, hidden = 5, method = "nnet")## # weights: 39
## initial value 90.443203
## iter 10 value 33.522949
## iter 20 value 29.081551
## iter 30 value 25.631000
## iter 40 value 25.517964
## iter 50 value 25.395198
## iter 60 value 25.275651
## iter 70 value 25.125594
## iter 80 value 25.083164
## iter 90 value 25.080909
## iter 100 value 25.080875
## final value 25.080875
## stopped after 100 iterations
## # weights: 109
## initial value 95.605172
## iter 10 value 31.720806
## iter 20 value 21.847426
## iter 30 value 18.902135
## iter 40 value 16.921231
## iter 50 value 16.448415
## iter 60 value 14.611412
## iter 70 value 14.256358
## iter 80 value 13.929841
## iter 90 value 4.909790
## iter 100 value 4.500195
## final value 4.500195
## stopped after 100 iterations
## # weights: 179
## initial value 115.225552
## iter 10 value 16.288091
## iter 20 value 4.194251
## iter 30 value 0.250901
## iter 40 value 0.023452
## iter 50 value 0.010547
## iter 60 value 0.002400
## iter 70 value 0.000788
## iter 80 value 0.000229
## iter 90 value 0.000140
## final value 0.000085
## converged
## # weights: 39
## initial value 99.122639
## iter 10 value 55.167500
## iter 20 value 44.898518
## iter 30 value 41.116677
## iter 40 value 40.029601
## iter 50 value 38.251906
## iter 60 value 36.067643
## iter 70 value 34.828719
## iter 80 value 34.678452
## final value 34.678386
## converged
## # weights: 109
## initial value 125.755935
## iter 10 value 49.563245
## iter 20 value 30.684360
## iter 30 value 17.693543
## iter 40 value 13.908672
## iter 50 value 13.614344
## iter 60 value 13.433069
## iter 70 value 13.086003
## iter 80 value 11.668672
## iter 90 value 11.218409
## iter 100 value 11.214485
## final value 11.214485
## stopped after 100 iterations
## # weights: 179
## initial value 91.656334
## iter 10 value 33.201743
## iter 20 value 12.681427
## iter 30 value 9.869531
## iter 40 value 9.214958
## iter 50 value 9.131412
## iter 60 value 9.066018
## iter 70 value 8.996023
## iter 80 value 8.895696
## iter 90 value 8.776035
## iter 100 value 8.769974
## final value 8.769974
## stopped after 100 iterations
## # weights: 39
## initial value 87.201502
## iter 10 value 41.199495
## iter 20 value 38.238237
## iter 30 value 37.143784
## iter 40 value 35.524119
## iter 50 value 35.018233
## iter 60 value 34.042314
## iter 70 value 28.907243
## iter 80 value 26.316429
## iter 90 value 25.263122
## iter 100 value 23.316918
## final value 23.316918
## stopped after 100 iterations
## # weights: 109
## initial value 83.188527
## iter 10 value 28.268240
## iter 20 value 21.936391
## iter 30 value 20.065398
## iter 40 value 18.630963
## iter 50 value 17.970056
## iter 60 value 13.959872
## iter 70 value 12.926596
## iter 80 value 12.745194
## iter 90 value 11.182971
## iter 100 value 10.897900
## final value 10.897900
## stopped after 100 iterations
## # weights: 179
## initial value 88.663771
## iter 10 value 11.817962
## iter 20 value 4.425132
## iter 30 value 4.356586
## iter 40 value 4.330111
## iter 50 value 3.455265
## iter 60 value 1.619995
## iter 70 value 1.546960
## iter 80 value 1.538855
## iter 90 value 1.520211
## iter 100 value 0.157552
## final value 0.157552
## stopped after 100 iterations
## # weights: 39
## initial value 94.783065
## iter 10 value 53.592345
## iter 20 value 47.413612
## iter 30 value 47.405922
## final value 47.405898
## converged
## # weights: 109
## initial value 87.084715
## iter 10 value 26.023255
## iter 20 value 10.114774
## iter 30 value 6.342580
## iter 40 value 2.451392
## iter 50 value 0.385445
## iter 60 value 0.004774
## final value 0.000074
## converged
## # weights: 179
## initial value 97.163515
## iter 10 value 2.318107
## iter 20 value 0.034487
## iter 30 value 0.003940
## iter 40 value 0.000306
## final value 0.000056
## converged
## # weights: 39
## initial value 86.806419
## iter 10 value 75.907903
## iter 20 value 54.688468
## iter 30 value 50.443480
## iter 40 value 46.229664
## iter 50 value 45.961644
## final value 45.958668
## converged
## # weights: 109
## initial value 85.421652
## iter 10 value 36.382018
## iter 20 value 18.181113
## iter 30 value 13.355138
## iter 40 value 12.954718
## iter 50 value 12.933512
## iter 60 value 12.932679
## iter 70 value 12.932642
## final value 12.932639
## converged
## # weights: 179
## initial value 83.841104
## iter 10 value 18.837481
## iter 20 value 11.161662
## iter 30 value 9.930130
## iter 40 value 9.566609
## iter 50 value 9.197280
## iter 60 value 9.032886
## iter 70 value 9.028200
## final value 9.028183
## converged
## # weights: 39
## initial value 84.493812
## iter 10 value 48.961837
## iter 20 value 47.120962
## iter 30 value 47.082685
## iter 40 value 47.041195
## iter 50 value 44.496694
## iter 60 value 41.900776
## iter 70 value 41.830720
## iter 80 value 41.820574
## iter 90 value 41.813915
## iter 100 value 39.622200
## final value 39.622200
## stopped after 100 iterations
## # weights: 109
## initial value 88.138094
## iter 10 value 21.747293
## iter 20 value 15.048828
## iter 30 value 4.739654
## iter 40 value 3.025010
## iter 50 value 0.210109
## iter 60 value 0.137009
## iter 70 value 0.124200
## iter 80 value 0.115496
## iter 90 value 0.102141
## iter 100 value 0.098121
## final value 0.098121
## stopped after 100 iterations
## # weights: 179
## initial value 91.373849
## iter 10 value 20.036539
## iter 20 value 16.034971
## iter 30 value 13.476093
## iter 40 value 8.681280
## iter 50 value 8.207184
## iter 60 value 8.188263
## iter 70 value 6.974597
## iter 80 value 0.151427
## iter 90 value 0.116454
## iter 100 value 0.109773
## final value 0.109773
## stopped after 100 iterations
## # weights: 39
## initial value 86.274487
## iter 10 value 54.969200
## iter 20 value 46.295371
## iter 30 value 43.451786
## iter 40 value 35.825469
## iter 50 value 30.188165
## iter 60 value 29.345780
## iter 70 value 29.332040
## iter 80 value 29.150480
## iter 90 value 27.963031
## iter 100 value 26.884173
## final value 26.884173
## stopped after 100 iterations
## # weights: 109
## initial value 80.318196
## iter 10 value 33.230696
## iter 20 value 29.954567
## iter 30 value 21.628038
## iter 40 value 19.174917
## iter 50 value 19.114320
## iter 60 value 19.108960
## iter 70 value 17.916887
## iter 80 value 17.492408
## iter 90 value 17.491062
## iter 100 value 16.469337
## final value 16.469337
## stopped after 100 iterations
## # weights: 179
## initial value 116.205737
## iter 10 value 25.710360
## iter 20 value 0.588619
## iter 30 value 0.001871
## iter 40 value 0.000492
## final value 0.000041
## converged
## # weights: 39
## initial value 85.707050
## iter 10 value 59.869708
## iter 20 value 50.880603
## iter 30 value 47.953242
## iter 40 value 47.821941
## iter 50 value 47.708402
## iter 60 value 45.499533
## iter 70 value 44.250235
## iter 80 value 44.003961
## final value 44.003956
## converged
## # weights: 109
## initial value 82.029705
## iter 10 value 43.266080
## iter 20 value 18.106270
## iter 30 value 14.673940
## iter 40 value 14.289562
## iter 50 value 14.087079
## iter 60 value 14.022807
## iter 70 value 14.002406
## iter 80 value 13.996983
## iter 90 value 13.817015
## iter 100 value 13.636175
## final value 13.636175
## stopped after 100 iterations
## # weights: 179
## initial value 98.834823
## iter 10 value 36.380523
## iter 20 value 16.515008
## iter 30 value 12.663731
## iter 40 value 10.549273
## iter 50 value 10.060107
## iter 60 value 9.870984
## iter 70 value 9.487019
## iter 80 value 9.401886
## iter 90 value 9.366593
## iter 100 value 9.363588
## final value 9.363588
## stopped after 100 iterations
## # weights: 39
## initial value 92.269604
## iter 10 value 45.213232
## iter 20 value 33.655342
## iter 30 value 25.408527
## iter 40 value 21.498229
## iter 50 value 19.126444
## iter 60 value 18.590005
## iter 70 value 17.211918
## iter 80 value 15.679628
## iter 90 value 15.526128
## iter 100 value 15.486981
## final value 15.486981
## stopped after 100 iterations
## # weights: 109
## initial value 86.556071
## iter 10 value 21.126144
## iter 20 value 12.730770
## iter 30 value 8.742250
## iter 40 value 5.295686
## iter 50 value 5.189398
## iter 60 value 2.870990
## iter 70 value 2.670458
## iter 80 value 2.568535
## iter 90 value 2.551348
## iter 100 value 2.543453
## final value 2.543453
## stopped after 100 iterations
## # weights: 179
## initial value 85.899796
## iter 10 value 19.404934
## iter 20 value 1.100866
## iter 30 value 0.267923
## iter 40 value 0.246658
## iter 50 value 0.208982
## iter 60 value 0.191440
## iter 70 value 0.169343
## iter 80 value 0.153520
## iter 90 value 0.129620
## iter 100 value 0.107696
## final value 0.107696
## stopped after 100 iterations
## # weights: 39
## initial value 85.430115
## iter 10 value 57.225354
## iter 20 value 53.384902
## iter 30 value 51.057077
## iter 40 value 50.925104
## iter 50 value 50.922126
## iter 60 value 50.921837
## iter 70 value 50.920673
## final value 50.920351
## converged
## # weights: 109
## initial value 85.167665
## iter 10 value 43.130540
## iter 20 value 23.554206
## iter 30 value 21.552249
## iter 40 value 20.072041
## iter 50 value 19.914727
## iter 60 value 19.902638
## iter 70 value 19.891377
## iter 80 value 16.626852
## iter 90 value 13.128356
## iter 100 value 12.955756
## final value 12.955756
## stopped after 100 iterations
## # weights: 179
## initial value 88.286280
## iter 10 value 4.681288
## iter 20 value 0.043541
## iter 30 value 0.018550
## iter 40 value 0.003462
## iter 50 value 0.001255
## final value 0.000069
## converged
## # weights: 39
## initial value 94.891027
## iter 10 value 55.411517
## iter 20 value 48.375786
## iter 30 value 46.518448
## iter 40 value 46.394271
## iter 50 value 46.219650
## iter 60 value 45.602791
## iter 70 value 45.547223
## final value 45.547177
## converged
## # weights: 109
## initial value 87.111745
## iter 10 value 30.400489
## iter 20 value 19.456495
## iter 30 value 14.712802
## iter 40 value 13.830381
## iter 50 value 13.701561
## iter 60 value 13.696985
## iter 70 value 13.696311
## iter 80 value 13.696173
## final value 13.696161
## converged
## # weights: 179
## initial value 104.196537
## iter 10 value 39.403897
## iter 20 value 17.959820
## iter 30 value 12.788810
## iter 40 value 10.185183
## iter 50 value 9.919076
## iter 60 value 9.778906
## iter 70 value 9.770507
## iter 80 value 9.770228
## iter 90 value 9.770166
## final value 9.770163
## converged
## # weights: 39
## initial value 88.889867
## iter 10 value 52.154530
## iter 20 value 47.815507
## iter 30 value 46.865824
## iter 40 value 46.568644
## iter 50 value 46.541225
## iter 60 value 45.922501
## iter 70 value 45.329131
## iter 80 value 45.327348
## iter 90 value 45.317741
## iter 100 value 45.311484
## final value 45.311484
## stopped after 100 iterations
## # weights: 109
## initial value 95.002202
## iter 10 value 28.105194
## iter 20 value 0.797318
## iter 30 value 0.283174
## iter 40 value 0.243165
## iter 50 value 0.213412
## iter 60 value 0.179481
## iter 70 value 0.158125
## iter 80 value 0.147890
## iter 90 value 0.138696
## iter 100 value 0.133682
## final value 0.133682
## stopped after 100 iterations
## # weights: 179
## initial value 94.277604
## iter 10 value 13.652133
## iter 20 value 4.123864
## iter 30 value 3.446456
## iter 40 value 3.020496
## iter 50 value 2.823374
## iter 60 value 2.772622
## iter 70 value 0.522470
## iter 80 value 0.237647
## iter 90 value 0.208239
## iter 100 value 0.139159
## final value 0.139159
## stopped after 100 iterations
## # weights: 39
## initial value 94.460149
## iter 10 value 51.389012
## iter 20 value 43.140962
## iter 30 value 33.502189
## iter 40 value 27.847599
## iter 50 value 26.824443
## iter 60 value 26.640600
## iter 70 value 26.619348
## iter 80 value 26.597036
## iter 90 value 26.590045
## iter 100 value 26.575277
## final value 26.575277
## stopped after 100 iterations
## # weights: 109
## initial value 90.391599
## iter 10 value 7.930567
## iter 20 value 3.633502
## iter 30 value 3.526730
## iter 40 value 3.443888
## iter 50 value 2.982633
## iter 60 value 0.006821
## iter 70 value 0.002643
## iter 80 value 0.000379
## iter 90 value 0.000223
## iter 100 value 0.000128
## final value 0.000128
## stopped after 100 iterations
## # weights: 179
## initial value 87.133673
## iter 10 value 12.481079
## iter 20 value 3.318230
## iter 30 value 1.831807
## iter 40 value 0.056883
## iter 50 value 0.013627
## iter 60 value 0.006736
## iter 70 value 0.003365
## iter 80 value 0.001944
## iter 90 value 0.001451
## iter 100 value 0.000798
## final value 0.000798
## stopped after 100 iterations
## # weights: 39
## initial value 88.563420
## iter 10 value 47.640507
## iter 20 value 44.315735
## iter 30 value 42.430785
## iter 40 value 41.526490
## iter 50 value 41.412117
## iter 60 value 40.455560
## iter 70 value 40.143907
## final value 40.139625
## converged
## # weights: 109
## initial value 85.979869
## iter 10 value 37.610561
## iter 20 value 18.706558
## iter 30 value 14.114685
## iter 40 value 13.069311
## iter 50 value 12.948879
## iter 60 value 12.924478
## iter 70 value 12.920273
## iter 80 value 12.843843
## iter 90 value 12.714622
## iter 100 value 12.711120
## final value 12.711120
## stopped after 100 iterations
## # weights: 179
## initial value 87.609021
## iter 10 value 29.697635
## iter 20 value 11.896680
## iter 30 value 9.670379
## iter 40 value 9.141076
## iter 50 value 9.085234
## iter 60 value 9.072865
## iter 70 value 9.072378
## iter 80 value 9.072302
## final value 9.072299
## converged
## # weights: 39
## initial value 84.685863
## iter 10 value 61.851769
## iter 20 value 42.560284
## iter 30 value 41.078396
## iter 40 value 40.657067
## iter 50 value 40.600519
## iter 60 value 40.594214
## iter 70 value 40.592003
## iter 80 value 40.580621
## iter 90 value 37.142350
## iter 100 value 35.353003
## final value 35.353003
## stopped after 100 iterations
## # weights: 109
## initial value 86.006150
## iter 10 value 12.014673
## iter 20 value 8.523674
## iter 30 value 4.504624
## iter 40 value 3.986184
## iter 50 value 3.940254
## iter 60 value 3.464024
## iter 70 value 3.362368
## iter 80 value 3.345236
## iter 90 value 3.301024
## iter 100 value 3.275278
## final value 3.275278
## stopped after 100 iterations
## # weights: 179
## initial value 91.636387
## iter 10 value 19.423609
## iter 20 value 2.315661
## iter 30 value 0.213472
## iter 40 value 0.181507
## iter 50 value 0.164156
## iter 60 value 0.143868
## iter 70 value 0.129505
## iter 80 value 0.119273
## iter 90 value 0.107597
## iter 100 value 0.100645
## final value 0.100645
## stopped after 100 iterations
## # weights: 39
## initial value 96.098385
## iter 10 value 39.375213
## iter 20 value 37.798208
## iter 30 value 37.538787
## iter 40 value 35.200981
## iter 50 value 33.220585
## iter 60 value 33.181587
## iter 70 value 33.164335
## iter 80 value 33.125487
## iter 90 value 33.021383
## iter 100 value 32.883628
## final value 32.883628
## stopped after 100 iterations
## # weights: 109
## initial value 100.613408
## iter 10 value 34.385032
## iter 20 value 22.629568
## iter 30 value 22.206673
## iter 40 value 22.099804
## iter 50 value 18.525970
## iter 60 value 18.522409
## final value 18.522345
## converged
## # weights: 179
## initial value 109.623683
## iter 10 value 18.936297
## iter 20 value 3.276659
## iter 30 value 2.780370
## iter 40 value 2.772626
## iter 50 value 2.772606
## final value 2.772589
## converged
## # weights: 39
## initial value 100.819689
## iter 10 value 66.110514
## iter 20 value 51.872886
## iter 30 value 47.839847
## iter 40 value 46.910595
## iter 50 value 44.475972
## iter 60 value 42.359488
## iter 70 value 42.182041
## iter 80 value 42.178825
## iter 90 value 41.954433
## iter 100 value 41.022153
## final value 41.022153
## stopped after 100 iterations
## # weights: 109
## initial value 83.972304
## iter 10 value 31.973653
## iter 20 value 18.513537
## iter 30 value 14.075397
## iter 40 value 13.374546
## iter 50 value 12.785221
## iter 60 value 11.878226
## iter 70 value 11.744054
## final value 11.743838
## converged
## # weights: 179
## initial value 75.849269
## iter 10 value 32.628312
## iter 20 value 14.725494
## iter 30 value 10.022148
## iter 40 value 9.189537
## iter 50 value 9.130171
## iter 60 value 9.080929
## iter 70 value 8.908773
## iter 80 value 8.841742
## final value 8.841481
## converged
## # weights: 39
## initial value 85.799499
## iter 10 value 43.617362
## iter 20 value 34.249951
## iter 30 value 31.305164
## iter 40 value 30.361464
## iter 50 value 30.290953
## iter 60 value 29.599607
## iter 70 value 28.454963
## iter 80 value 26.964606
## iter 90 value 26.504183
## iter 100 value 26.233920
## final value 26.233920
## stopped after 100 iterations
## # weights: 109
## initial value 84.346112
## iter 10 value 31.238751
## iter 20 value 24.851702
## iter 30 value 17.040986
## iter 40 value 16.468365
## iter 50 value 14.804783
## iter 60 value 14.706728
## iter 70 value 14.691470
## iter 80 value 11.901883
## iter 90 value 7.924900
## iter 100 value 5.185011
## final value 5.185011
## stopped after 100 iterations
## # weights: 179
## initial value 135.816014
## iter 10 value 28.633434
## iter 20 value 3.210789
## iter 30 value 0.332123
## iter 40 value 0.287206
## iter 50 value 0.267787
## iter 60 value 0.241176
## iter 70 value 0.204326
## iter 80 value 0.161146
## iter 90 value 0.133226
## iter 100 value 0.123824
## final value 0.123824
## stopped after 100 iterations
## # weights: 39
## initial value 84.555277
## iter 10 value 52.401706
## iter 20 value 46.716274
## iter 30 value 44.489603
## iter 40 value 41.728293
## iter 50 value 41.441095
## iter 60 value 40.317774
## iter 70 value 40.163819
## iter 80 value 40.090193
## iter 90 value 40.084579
## iter 100 value 40.084029
## final value 40.084029
## stopped after 100 iterations
## # weights: 109
## initial value 84.461243
## iter 10 value 25.676559
## iter 20 value 10.681158
## iter 30 value 10.241056
## iter 40 value 9.941434
## iter 50 value 9.937546
## iter 60 value 9.937409
## final value 9.937406
## converged
## # weights: 179
## initial value 85.647588
## iter 10 value 1.113502
## iter 20 value 0.015207
## iter 30 value 0.001123
## iter 40 value 0.000121
## final value 0.000095
## converged
## # weights: 39
## initial value 84.493059
## iter 10 value 55.337174
## iter 20 value 49.473363
## iter 30 value 45.855050
## iter 40 value 45.236948
## iter 50 value 45.231476
## final value 45.231474
## converged
## # weights: 109
## initial value 88.576805
## iter 10 value 47.901369
## iter 20 value 19.973965
## iter 30 value 16.340185
## iter 40 value 14.075666
## iter 50 value 13.241925
## iter 60 value 13.230843
## final value 13.230825
## converged
## # weights: 179
## initial value 96.244694
## iter 10 value 36.815255
## iter 20 value 16.444696
## iter 30 value 13.402294
## iter 40 value 10.660775
## iter 50 value 9.741046
## iter 60 value 9.718537
## iter 70 value 9.717106
## iter 80 value 9.717017
## iter 90 value 9.717015
## final value 9.717014
## converged
## # weights: 39
## initial value 87.527507
## iter 10 value 59.470647
## iter 20 value 54.261550
## iter 30 value 50.852477
## iter 40 value 43.821573
## iter 50 value 37.665738
## iter 60 value 34.944475
## iter 70 value 32.372165
## iter 80 value 30.552119
## iter 90 value 29.890023
## iter 100 value 29.628402
## final value 29.628402
## stopped after 100 iterations
## # weights: 109
## initial value 95.897478
## iter 10 value 19.551051
## iter 20 value 6.736674
## iter 30 value 0.306956
## iter 40 value 0.151007
## iter 50 value 0.130962
## iter 60 value 0.125228
## iter 70 value 0.119796
## iter 80 value 0.114569
## iter 90 value 0.104433
## iter 100 value 0.097994
## final value 0.097994
## stopped after 100 iterations
## # weights: 179
## initial value 92.894485
## iter 10 value 7.813962
## iter 20 value 2.245462
## iter 30 value 2.056088
## iter 40 value 1.739702
## iter 50 value 1.564327
## iter 60 value 1.539055
## iter 70 value 0.191459
## iter 80 value 0.153090
## iter 90 value 0.143226
## iter 100 value 0.120820
## final value 0.120820
## stopped after 100 iterations
## # weights: 39
## initial value 82.045220
## iter 10 value 50.460103
## iter 20 value 40.359635
## iter 30 value 39.868212
## iter 40 value 39.801420
## iter 50 value 39.796008
## iter 60 value 39.777249
## iter 70 value 39.744564
## iter 80 value 39.715937
## final value 39.700620
## converged
## # weights: 109
## initial value 93.497383
## iter 10 value 23.968601
## iter 20 value 11.199831
## iter 30 value 9.462070
## iter 40 value 9.142694
## iter 50 value 9.125637
## iter 60 value 9.124723
## iter 70 value 9.124622
## iter 80 value 9.124597
## final value 9.124593
## converged
## # weights: 179
## initial value 89.102480
## iter 10 value 2.568511
## iter 20 value 0.004981
## iter 30 value 0.000125
## final value 0.000087
## converged
## # weights: 39
## initial value 90.422485
## iter 10 value 55.439180
## iter 20 value 51.210726
## iter 30 value 49.466750
## iter 40 value 47.377306
## iter 50 value 46.510887
## iter 60 value 46.510403
## iter 70 value 46.505202
## iter 80 value 45.837570
## iter 90 value 44.594694
## final value 44.551711
## converged
## # weights: 109
## initial value 84.935148
## iter 10 value 40.757184
## iter 20 value 19.629173
## iter 30 value 15.348745
## iter 40 value 12.681702
## iter 50 value 12.199866
## iter 60 value 12.185067
## iter 70 value 12.184624
## iter 80 value 12.184611
## iter 80 value 12.184611
## iter 80 value 12.184611
## final value 12.184611
## converged
## # weights: 179
## initial value 83.910426
## iter 10 value 34.998274
## iter 20 value 15.773805
## iter 30 value 12.113621
## iter 40 value 10.561321
## iter 50 value 10.240869
## iter 60 value 10.221188
## iter 70 value 10.218426
## iter 80 value 10.217704
## iter 90 value 10.217680
## final value 10.217679
## converged
## # weights: 39
## initial value 87.161684
## iter 10 value 48.919987
## iter 20 value 37.120230
## iter 30 value 33.619473
## iter 40 value 31.964313
## iter 50 value 31.846112
## iter 60 value 31.833067
## iter 70 value 31.788296
## iter 80 value 31.785319
## iter 90 value 31.771949
## iter 100 value 31.770205
## final value 31.770205
## stopped after 100 iterations
## # weights: 109
## initial value 84.472374
## iter 10 value 24.637282
## iter 20 value 6.256709
## iter 30 value 3.710129
## iter 40 value 2.301789
## iter 50 value 0.196754
## iter 60 value 0.154125
## iter 70 value 0.142200
## iter 80 value 0.124523
## iter 90 value 0.110449
## iter 100 value 0.101403
## final value 0.101403
## stopped after 100 iterations
## # weights: 179
## initial value 86.311468
## iter 10 value 19.606938
## iter 20 value 0.175800
## iter 30 value 0.140464
## iter 40 value 0.127027
## iter 50 value 0.105444
## iter 60 value 0.088970
## iter 70 value 0.081223
## iter 80 value 0.078218
## iter 90 value 0.073560
## iter 100 value 0.068932
## final value 0.068932
## stopped after 100 iterations
## # weights: 39
## initial value 95.240893
## iter 10 value 54.335887
## iter 20 value 44.230801
## iter 30 value 40.626475
## iter 40 value 40.607851
## final value 40.607827
## converged
## # weights: 109
## initial value 91.822970
## iter 10 value 33.620848
## iter 20 value 21.077220
## iter 30 value 17.886135
## iter 40 value 10.261788
## iter 50 value 5.746909
## iter 60 value 3.450583
## iter 70 value 3.343943
## iter 80 value 3.015293
## iter 90 value 3.014907
## iter 100 value 3.014293
## final value 3.014293
## stopped after 100 iterations
## # weights: 179
## initial value 78.654200
## iter 10 value 21.477523
## iter 20 value 5.268091
## iter 30 value 2.269101
## iter 40 value 0.116834
## iter 50 value 0.041772
## iter 60 value 0.002297
## iter 70 value 0.000695
## iter 80 value 0.000405
## final value 0.000078
## converged
## # weights: 39
## initial value 88.612030
## iter 10 value 49.622789
## iter 20 value 46.781365
## iter 30 value 45.469918
## iter 40 value 44.221383
## iter 50 value 43.291555
## iter 60 value 42.952889
## iter 70 value 42.777443
## iter 80 value 41.746366
## iter 90 value 41.701759
## final value 41.701757
## converged
## # weights: 109
## initial value 112.009100
## iter 10 value 39.822394
## iter 20 value 18.341515
## iter 30 value 13.739736
## iter 40 value 12.873716
## iter 50 value 12.801245
## iter 60 value 12.730344
## iter 70 value 12.373070
## iter 80 value 12.300504
## final value 12.300475
## converged
## # weights: 179
## initial value 90.899929
## iter 10 value 25.380800
## iter 20 value 11.075211
## iter 30 value 9.434821
## iter 40 value 9.309890
## iter 50 value 9.293179
## iter 60 value 9.292766
## final value 9.292761
## converged
## # weights: 39
## initial value 84.660999
## iter 10 value 49.398835
## iter 20 value 38.087474
## iter 30 value 32.891198
## iter 40 value 32.856073
## iter 50 value 32.853953
## iter 60 value 32.842141
## iter 70 value 32.841166
## iter 80 value 32.535814
## iter 90 value 26.878178
## iter 100 value 22.543424
## final value 22.543424
## stopped after 100 iterations
## # weights: 109
## initial value 81.280071
## iter 10 value 20.730092
## iter 20 value 12.601547
## iter 30 value 10.933330
## iter 40 value 10.309970
## iter 50 value 10.261066
## iter 60 value 10.249574
## iter 70 value 10.232252
## iter 80 value 10.220141
## iter 90 value 9.603298
## iter 100 value 5.631390
## final value 5.631390
## stopped after 100 iterations
## # weights: 179
## initial value 92.642853
## iter 10 value 18.447145
## iter 20 value 1.291731
## iter 30 value 0.247518
## iter 40 value 0.176579
## iter 50 value 0.147135
## iter 60 value 0.125422
## iter 70 value 0.111623
## iter 80 value 0.098209
## iter 90 value 0.090113
## iter 100 value 0.085956
## final value 0.085956
## stopped after 100 iterations
## # weights: 39
## initial value 84.634485
## iter 10 value 60.329517
## iter 20 value 48.805937
## iter 30 value 47.747798
## iter 40 value 47.676858
## iter 50 value 47.667324
## iter 60 value 47.666017
## iter 70 value 47.665841
## final value 47.665820
## converged
## # weights: 109
## initial value 76.844182
## iter 10 value 12.473045
## iter 20 value 6.860033
## iter 30 value 6.482738
## iter 40 value 6.228858
## iter 50 value 5.926822
## iter 60 value 5.830490
## iter 70 value 5.738519
## iter 80 value 5.499911
## iter 90 value 5.113787
## iter 100 value 5.040150
## final value 5.040150
## stopped after 100 iterations
## # weights: 179
## initial value 97.227967
## iter 10 value 28.335348
## iter 20 value 13.570519
## iter 30 value 0.172348
## iter 40 value 0.008744
## iter 50 value 0.001066
## iter 60 value 0.000187
## final value 0.000086
## converged
## # weights: 39
## initial value 99.102172
## iter 10 value 62.321742
## iter 20 value 43.079288
## iter 30 value 41.755140
## iter 40 value 41.708582
## iter 50 value 41.345610
## iter 60 value 39.218102
## iter 70 value 38.195904
## iter 80 value 37.834449
## iter 90 value 37.409385
## iter 100 value 37.207320
## final value 37.207320
## stopped after 100 iterations
## # weights: 109
## initial value 79.457797
## iter 10 value 42.195002
## iter 20 value 23.916126
## iter 30 value 14.692237
## iter 40 value 13.636215
## iter 50 value 13.571933
## iter 60 value 13.570770
## final value 13.570758
## converged
## # weights: 179
## initial value 80.324008
## iter 10 value 26.593789
## iter 20 value 13.256101
## iter 30 value 10.056126
## iter 40 value 9.635744
## iter 50 value 9.599798
## iter 60 value 9.598230
## iter 70 value 9.598190
## final value 9.598190
## converged
## # weights: 39
## initial value 106.372760
## iter 10 value 50.764720
## iter 20 value 45.703467
## iter 30 value 45.118499
## iter 40 value 44.996638
## iter 50 value 44.977079
## iter 60 value 44.163350
## iter 70 value 44.138066
## iter 80 value 43.771994
## iter 90 value 42.693493
## iter 100 value 41.801180
## final value 41.801180
## stopped after 100 iterations
## # weights: 109
## initial value 94.368265
## iter 10 value 37.537841
## iter 20 value 9.237348
## iter 30 value 5.607793
## iter 40 value 3.957762
## iter 50 value 3.582007
## iter 60 value 3.457202
## iter 70 value 3.422662
## iter 80 value 3.420760
## iter 90 value 3.118252
## iter 100 value 2.962920
## final value 2.962920
## stopped after 100 iterations
## # weights: 179
## initial value 77.328495
## iter 10 value 6.997263
## iter 20 value 6.087295
## iter 30 value 2.731081
## iter 40 value 2.697898
## iter 50 value 2.625033
## iter 60 value 0.303728
## iter 70 value 0.196101
## iter 80 value 0.179384
## iter 90 value 0.170934
## iter 100 value 0.143402
## final value 0.143402
## stopped after 100 iterations
## # weights: 39
## initial value 87.726434
## iter 10 value 56.789121
## iter 20 value 51.328945
## iter 30 value 50.727551
## iter 40 value 50.565547
## iter 50 value 50.544471
## iter 60 value 50.541772
## iter 70 value 50.514281
## iter 80 value 50.493113
## iter 90 value 50.492645
## final value 50.492608
## converged
## # weights: 109
## initial value 85.146082
## iter 10 value 17.247189
## iter 20 value 4.934125
## iter 30 value 1.789466
## iter 40 value 0.057120
## iter 50 value 0.001257
## iter 60 value 0.000343
## iter 70 value 0.000103
## iter 70 value 0.000091
## iter 70 value 0.000091
## final value 0.000091
## converged
## # weights: 179
## initial value 91.033007
## iter 10 value 31.708816
## iter 20 value 18.209857
## iter 30 value 13.518443
## iter 40 value 11.694392
## iter 50 value 6.317442
## iter 60 value 6.163341
## iter 70 value 5.388519
## iter 80 value 5.072793
## iter 90 value 4.812263
## iter 100 value 4.684259
## final value 4.684259
## stopped after 100 iterations
## # weights: 39
## initial value 90.979391
## iter 10 value 63.840346
## iter 20 value 52.615424
## iter 30 value 45.795996
## iter 40 value 43.481910
## iter 50 value 43.400406
## final value 43.400337
## converged
## # weights: 109
## initial value 94.210037
## iter 10 value 34.748607
## iter 20 value 17.477748
## iter 30 value 13.690121
## iter 40 value 13.313755
## iter 50 value 13.103820
## iter 60 value 12.300829
## iter 70 value 12.274187
## final value 12.273916
## converged
## # weights: 179
## initial value 107.781479
## iter 10 value 46.113231
## iter 20 value 19.705543
## iter 30 value 13.620713
## iter 40 value 11.207289
## iter 50 value 10.508541
## iter 60 value 10.257546
## iter 70 value 10.057342
## iter 80 value 9.993389
## iter 90 value 9.980312
## iter 100 value 9.979237
## final value 9.979237
## stopped after 100 iterations
## # weights: 39
## initial value 85.637996
## iter 10 value 51.653682
## iter 20 value 47.024852
## iter 30 value 46.141526
## iter 40 value 44.813804
## iter 50 value 43.907115
## iter 60 value 43.822581
## iter 70 value 41.708388
## iter 80 value 37.321295
## iter 90 value 37.050755
## iter 100 value 36.959947
## final value 36.959947
## stopped after 100 iterations
## # weights: 109
## initial value 101.922916
## iter 10 value 40.093598
## iter 20 value 26.172665
## iter 30 value 23.959575
## iter 40 value 22.213973
## iter 50 value 21.606453
## iter 60 value 20.312927
## iter 70 value 19.728491
## iter 80 value 19.199098
## iter 90 value 18.996476
## iter 100 value 18.489666
## final value 18.489666
## stopped after 100 iterations
## # weights: 179
## initial value 88.500525
## iter 10 value 16.236081
## iter 20 value 9.359549
## iter 30 value 9.170230
## iter 40 value 9.113127
## iter 50 value 9.100655
## iter 60 value 8.683624
## iter 70 value 7.757516
## iter 80 value 4.253156
## iter 90 value 1.718618
## iter 100 value 1.557530
## final value 1.557530
## stopped after 100 iterations
## # weights: 39
## initial value 85.504158
## iter 10 value 51.035108
## iter 20 value 39.313031
## iter 30 value 37.099374
## iter 40 value 32.067650
## iter 50 value 24.718688
## iter 60 value 23.051059
## iter 70 value 22.770872
## iter 80 value 22.262695
## iter 90 value 17.880586
## iter 100 value 17.030609
## final value 17.030609
## stopped after 100 iterations
## # weights: 109
## initial value 86.368387
## iter 10 value 19.955508
## iter 20 value 3.918924
## iter 30 value 3.866134
## iter 40 value 3.862093
## final value 3.862085
## converged
## # weights: 179
## initial value 93.404603
## iter 10 value 6.995081
## iter 20 value 0.032685
## iter 30 value 0.000865
## final value 0.000054
## converged
## # weights: 39
## initial value 88.314914
## iter 10 value 55.186139
## iter 20 value 48.766242
## iter 30 value 46.880364
## iter 40 value 44.863689
## iter 50 value 44.716557
## final value 44.715521
## converged
## # weights: 109
## initial value 84.554258
## iter 10 value 42.769099
## iter 20 value 23.746488
## iter 30 value 15.237052
## iter 40 value 13.910516
## iter 50 value 13.855195
## iter 60 value 13.498744
## iter 70 value 13.340045
## final value 13.339054
## converged
## # weights: 179
## initial value 89.700336
## iter 10 value 19.369542
## iter 20 value 14.319760
## iter 30 value 11.368265
## iter 40 value 10.238950
## iter 50 value 10.019604
## iter 60 value 9.545527
## iter 70 value 9.456907
## iter 80 value 9.454461
## final value 9.454459
## converged
## # weights: 39
## initial value 83.217860
## iter 10 value 72.301523
## iter 20 value 54.547624
## iter 30 value 52.650064
## iter 40 value 48.000311
## iter 50 value 43.628001
## iter 60 value 41.230736
## iter 70 value 40.589780
## iter 80 value 40.319703
## iter 90 value 40.260977
## iter 100 value 33.202884
## final value 33.202884
## stopped after 100 iterations
## # weights: 109
## initial value 88.484848
## iter 10 value 27.234702
## iter 20 value 21.714300
## iter 30 value 21.677894
## iter 40 value 21.668467
## iter 50 value 21.650414
## iter 60 value 17.602318
## iter 70 value 15.493875
## iter 80 value 13.471855
## iter 90 value 0.548009
## iter 100 value 0.319634
## final value 0.319634
## stopped after 100 iterations
## # weights: 179
## initial value 85.158137
## iter 10 value 12.325689
## iter 20 value 5.530979
## iter 30 value 5.478463
## iter 40 value 2.125036
## iter 50 value 0.324598
## iter 60 value 0.196629
## iter 70 value 0.176477
## iter 80 value 0.156429
## iter 90 value 0.135107
## iter 100 value 0.117466
## final value 0.117466
## stopped after 100 iterations
## # weights: 39
## initial value 86.135040
## iter 10 value 58.849612
## iter 20 value 58.394005
## iter 30 value 57.215602
## iter 40 value 57.133695
## iter 50 value 56.957008
## iter 60 value 56.945747
## iter 70 value 56.203676
## iter 80 value 56.199832
## final value 56.199496
## converged
## # weights: 109
## initial value 85.263801
## iter 10 value 27.988399
## iter 20 value 10.459205
## iter 30 value 7.577709
## iter 40 value 7.554635
## iter 50 value 7.553103
## iter 60 value 7.553016
## iter 70 value 7.552982
## iter 80 value 7.552955
## final value 7.552946
## converged
## # weights: 179
## initial value 83.454380
## iter 10 value 5.220893
## iter 20 value 0.029194
## iter 30 value 0.000437
## iter 40 value 0.000113
## final value 0.000098
## converged
## # weights: 39
## initial value 85.070266
## iter 10 value 54.291042
## iter 20 value 50.846707
## iter 30 value 48.493029
## iter 40 value 43.253338
## iter 50 value 40.909423
## iter 60 value 40.789941
## final value 40.789597
## converged
## # weights: 109
## initial value 94.846875
## iter 10 value 45.152128
## iter 20 value 20.695816
## iter 30 value 13.259740
## iter 40 value 12.642599
## iter 50 value 12.606915
## iter 60 value 12.605400
## final value 12.605382
## converged
## # weights: 179
## initial value 99.763452
## iter 10 value 25.733380
## iter 20 value 11.276527
## iter 30 value 9.864735
## iter 40 value 8.510892
## iter 50 value 8.339314
## iter 60 value 8.330291
## iter 70 value 8.330149
## final value 8.330147
## converged
## # weights: 39
## initial value 80.735696
## iter 10 value 38.502479
## iter 20 value 38.032847
## iter 30 value 36.681442
## iter 40 value 33.928468
## iter 50 value 31.669356
## iter 60 value 31.330768
## iter 70 value 31.319876
## iter 80 value 31.309714
## iter 90 value 31.302838
## iter 100 value 31.300966
## final value 31.300966
## stopped after 100 iterations
## # weights: 109
## initial value 88.127854
## iter 10 value 48.603377
## iter 20 value 24.800636
## iter 30 value 21.634162
## iter 40 value 15.363698
## iter 50 value 14.444290
## iter 60 value 11.549834
## iter 70 value 10.599064
## iter 80 value 10.538604
## iter 90 value 10.484701
## iter 100 value 10.336191
## final value 10.336191
## stopped after 100 iterations
## # weights: 179
## initial value 82.382368
## iter 10 value 3.335850
## iter 20 value 0.139071
## iter 30 value 0.126641
## iter 40 value 0.119501
## iter 50 value 0.110842
## iter 60 value 0.100903
## iter 70 value 0.092511
## iter 80 value 0.084827
## iter 90 value 0.079327
## iter 100 value 0.072589
## final value 0.072589
## stopped after 100 iterations
## # weights: 39
## initial value 88.569429
## iter 10 value 52.057311
## iter 20 value 49.489339
## iter 30 value 49.478717
## final value 49.478695
## converged
## # weights: 109
## initial value 85.658770
## iter 10 value 12.616459
## iter 20 value 9.979147
## iter 30 value 6.967466
## iter 40 value 5.472989
## iter 50 value 5.439773
## iter 60 value 5.436268
## iter 70 value 5.435758
## iter 80 value 5.435403
## iter 90 value 5.435114
## iter 100 value 5.435092
## final value 5.435092
## stopped after 100 iterations
## # weights: 179
## initial value 94.014756
## iter 10 value 9.614060
## iter 20 value 0.234099
## iter 30 value 0.022176
## iter 40 value 0.001376
## final value 0.000092
## converged
## # weights: 39
## initial value 87.228509
## iter 10 value 57.631346
## iter 20 value 51.427902
## iter 30 value 43.972267
## iter 40 value 40.559650
## iter 50 value 39.313853
## iter 60 value 39.002215
## iter 70 value 38.973746
## final value 38.973734
## converged
## # weights: 109
## initial value 84.306901
## iter 10 value 34.525259
## iter 20 value 20.977475
## iter 30 value 14.037339
## iter 40 value 13.771425
## iter 50 value 13.716870
## iter 60 value 13.671838
## iter 70 value 13.584142
## iter 80 value 13.559821
## iter 90 value 13.559421
## final value 13.559420
## converged
## # weights: 179
## initial value 92.205157
## iter 10 value 25.996815
## iter 20 value 10.571112
## iter 30 value 8.990252
## iter 40 value 8.840946
## iter 50 value 8.836100
## final value 8.836079
## converged
## # weights: 39
## initial value 96.536657
## iter 10 value 47.667920
## iter 20 value 40.664939
## iter 30 value 35.682169
## iter 40 value 32.022733
## iter 50 value 30.912803
## iter 60 value 28.477832
## iter 70 value 27.498188
## iter 80 value 24.424616
## iter 90 value 24.287537
## iter 100 value 23.689402
## final value 23.689402
## stopped after 100 iterations
## # weights: 109
## initial value 97.964712
## iter 10 value 16.029815
## iter 20 value 13.199702
## iter 30 value 8.474735
## iter 40 value 7.036124
## iter 50 value 6.142626
## iter 60 value 5.602834
## iter 70 value 5.560212
## iter 80 value 5.551014
## iter 90 value 4.896932
## iter 100 value 4.160753
## final value 4.160753
## stopped after 100 iterations
## # weights: 179
## initial value 110.375823
## iter 10 value 8.369583
## iter 20 value 5.567377
## iter 30 value 3.875420
## iter 40 value 2.057444
## iter 50 value 2.037662
## iter 60 value 2.021935
## iter 70 value 0.178508
## iter 80 value 0.114990
## iter 90 value 0.110359
## iter 100 value 0.105149
## final value 0.105149
## stopped after 100 iterations
## # weights: 39
## initial value 80.957537
## iter 10 value 50.055387
## iter 20 value 41.115415
## iter 30 value 40.857893
## final value 40.857541
## converged
## # weights: 109
## initial value 82.797846
## iter 10 value 14.267774
## iter 20 value 8.640742
## iter 30 value 8.491461
## iter 40 value 7.934376
## iter 50 value 6.920440
## iter 60 value 6.087126
## iter 70 value 5.896673
## iter 80 value 5.659031
## iter 90 value 4.319553
## iter 100 value 4.208385
## final value 4.208385
## stopped after 100 iterations
## # weights: 179
## initial value 101.835834
## iter 10 value 26.695753
## iter 20 value 0.251809
## iter 30 value 0.006019
## iter 40 value 0.001160
## iter 50 value 0.000163
## iter 50 value 0.000087
## iter 50 value 0.000087
## final value 0.000087
## converged
## # weights: 39
## initial value 89.748768
## iter 10 value 64.560955
## iter 20 value 56.987643
## iter 30 value 52.296687
## iter 40 value 48.628113
## iter 50 value 47.283428
## iter 60 value 46.072337
## iter 70 value 45.582333
## final value 45.578561
## converged
## # weights: 109
## initial value 101.878225
## iter 10 value 32.576616
## iter 20 value 17.956448
## iter 30 value 14.510480
## iter 40 value 13.695570
## iter 50 value 13.353245
## iter 60 value 13.340087
## iter 70 value 13.339678
## final value 13.339662
## converged
## # weights: 179
## initial value 104.304063
## iter 10 value 22.654255
## iter 20 value 11.808058
## iter 30 value 10.135618
## iter 40 value 9.584948
## iter 50 value 9.553848
## final value 9.553761
## converged
## # weights: 39
## initial value 89.619804
## iter 10 value 54.870380
## iter 20 value 50.080364
## iter 30 value 49.220888
## iter 40 value 46.067817
## iter 50 value 44.704431
## iter 60 value 44.500894
## iter 70 value 44.248418
## iter 80 value 43.983202
## iter 90 value 43.912536
## iter 100 value 43.897615
## final value 43.897615
## stopped after 100 iterations
## # weights: 109
## initial value 82.148497
## iter 10 value 18.504338
## iter 20 value 16.534819
## iter 30 value 12.146233
## iter 40 value 10.851174
## iter 50 value 3.072431
## iter 60 value 0.404501
## iter 70 value 0.295789
## iter 80 value 0.275741
## iter 90 value 0.268108
## iter 100 value 0.256282
## final value 0.256282
## stopped after 100 iterations
## # weights: 179
## initial value 90.879574
## iter 10 value 12.276186
## iter 20 value 0.175685
## iter 30 value 0.135455
## iter 40 value 0.118041
## iter 50 value 0.110250
## iter 60 value 0.099248
## iter 70 value 0.090586
## iter 80 value 0.086845
## iter 90 value 0.084314
## iter 100 value 0.079695
## final value 0.079695
## stopped after 100 iterations
## # weights: 39
## initial value 90.885403
## iter 10 value 50.235584
## iter 20 value 41.293811
## iter 30 value 32.253235
## iter 40 value 28.547412
## iter 50 value 28.050527
## iter 60 value 28.023552
## iter 70 value 27.248049
## iter 80 value 26.701332
## iter 90 value 25.362784
## iter 100 value 24.804655
## final value 24.804655
## stopped after 100 iterations
## # weights: 109
## initial value 90.689643
## iter 10 value 44.000867
## iter 20 value 21.982741
## iter 30 value 19.441264
## iter 40 value 18.373513
## iter 50 value 16.517106
## iter 60 value 16.284408
## iter 70 value 15.712875
## iter 80 value 8.560844
## iter 90 value 6.663566
## iter 100 value 6.466321
## final value 6.466321
## stopped after 100 iterations
## # weights: 179
## initial value 99.004364
## iter 10 value 13.336342
## iter 20 value 0.175991
## iter 30 value 0.001295
## final value 0.000090
## converged
## # weights: 39
## initial value 87.859719
## iter 10 value 55.557296
## iter 20 value 50.743608
## iter 30 value 46.335764
## iter 40 value 45.690365
## iter 50 value 45.687632
## iter 50 value 45.687632
## iter 50 value 45.687632
## final value 45.687632
## converged
## # weights: 109
## initial value 95.752011
## iter 10 value 33.296873
## iter 20 value 15.201010
## iter 30 value 13.355825
## iter 40 value 12.000015
## iter 50 value 11.969858
## final value 11.969847
## converged
## # weights: 179
## initial value 88.645177
## iter 10 value 35.025365
## iter 20 value 12.234525
## iter 30 value 10.146700
## iter 40 value 9.608180
## iter 50 value 9.576469
## iter 60 value 9.527474
## iter 70 value 9.500696
## final value 9.500285
## converged
## # weights: 39
## initial value 84.162095
## iter 10 value 45.208478
## iter 20 value 36.056182
## iter 30 value 35.668986
## iter 40 value 35.645635
## iter 50 value 35.635089
## iter 60 value 33.746947
## iter 70 value 33.132181
## iter 80 value 33.070633
## iter 90 value 30.685026
## iter 100 value 30.559358
## final value 30.559358
## stopped after 100 iterations
## # weights: 109
## initial value 98.288410
## iter 10 value 64.716515
## iter 20 value 27.328697
## iter 30 value 23.962148
## iter 40 value 16.745104
## iter 50 value 13.808099
## iter 60 value 12.894992
## iter 70 value 12.861144
## iter 80 value 12.841487
## iter 90 value 12.799720
## iter 100 value 10.470363
## final value 10.470363
## stopped after 100 iterations
## # weights: 179
## initial value 92.586932
## iter 10 value 5.577873
## iter 20 value 2.646779
## iter 30 value 1.578574
## iter 40 value 0.448454
## iter 50 value 0.287208
## iter 60 value 0.260003
## iter 70 value 0.213915
## iter 80 value 0.156713
## iter 90 value 0.137436
## iter 100 value 0.122577
## final value 0.122577
## stopped after 100 iterations
## # weights: 39
## initial value 82.285651
## iter 10 value 49.576909
## iter 20 value 43.496566
## iter 30 value 43.367276
## iter 40 value 41.968308
## iter 50 value 40.155264
## iter 60 value 40.150604
## final value 40.150354
## converged
## # weights: 109
## initial value 81.113319
## iter 10 value 15.710306
## iter 20 value 8.650494
## iter 30 value 6.481966
## iter 40 value 6.407040
## iter 50 value 6.401020
## iter 60 value 6.397249
## iter 70 value 6.394094
## iter 80 value 6.393849
## iter 90 value 6.392790
## iter 100 value 6.227954
## final value 6.227954
## stopped after 100 iterations
## # weights: 179
## initial value 74.165043
## iter 10 value 10.374266
## iter 20 value 2.205221
## iter 30 value 1.433166
## iter 40 value 1.388080
## iter 50 value 1.386627
## iter 60 value 1.386295
## final value 1.386295
## converged
## # weights: 39
## initial value 78.806613
## iter 10 value 50.157954
## iter 20 value 43.376447
## iter 30 value 42.305114
## iter 40 value 40.775329
## iter 50 value 39.388855
## iter 60 value 38.689967
## iter 70 value 38.656501
## final value 38.656137
## converged
## # weights: 109
## initial value 89.419148
## iter 10 value 41.428152
## iter 20 value 25.601646
## iter 30 value 17.834672
## iter 40 value 12.642925
## iter 50 value 11.595612
## iter 60 value 10.358178
## iter 70 value 10.321986
## iter 80 value 10.321725
## final value 10.321724
## converged
## # weights: 179
## initial value 83.130812
## iter 10 value 24.345634
## iter 20 value 10.714409
## iter 30 value 8.870532
## iter 40 value 8.533697
## iter 50 value 8.509163
## iter 60 value 8.497566
## iter 70 value 8.497112
## final value 8.497104
## converged
## # weights: 39
## initial value 82.915363
## iter 10 value 44.195114
## iter 20 value 39.187604
## iter 30 value 38.445664
## iter 40 value 38.413880
## iter 50 value 38.408266
## iter 60 value 38.391194
## iter 70 value 33.912380
## iter 80 value 33.198933
## iter 90 value 33.098916
## iter 100 value 33.006130
## final value 33.006130
## stopped after 100 iterations
## # weights: 109
## initial value 75.536522
## iter 10 value 24.378592
## iter 20 value 13.316891
## iter 30 value 11.949264
## iter 40 value 11.488736
## iter 50 value 11.146335
## iter 60 value 10.409765
## iter 70 value 10.012196
## iter 80 value 9.595245
## iter 90 value 6.528174
## iter 100 value 6.020460
## final value 6.020460
## stopped after 100 iterations
## # weights: 179
## initial value 91.459843
## iter 10 value 17.012083
## iter 20 value 0.383727
## iter 30 value 0.184435
## iter 40 value 0.145075
## iter 50 value 0.118865
## iter 60 value 0.095490
## iter 70 value 0.087759
## iter 80 value 0.083136
## iter 90 value 0.078991
## iter 100 value 0.075963
## final value 0.075963
## stopped after 100 iterations
## # weights: 39
## initial value 86.925422
## iter 10 value 75.089706
## iter 20 value 51.597660
## iter 30 value 50.322401
## iter 40 value 50.135663
## iter 50 value 49.890367
## iter 60 value 49.469059
## iter 70 value 48.380570
## iter 80 value 48.070010
## iter 90 value 45.818251
## iter 100 value 45.380477
## final value 45.380477
## stopped after 100 iterations
## # weights: 109
## initial value 80.216185
## iter 10 value 10.797088
## iter 20 value 0.492589
## iter 30 value 0.023043
## iter 40 value 0.003348
## iter 50 value 0.000462
## iter 60 value 0.000189
## final value 0.000085
## converged
## # weights: 179
## initial value 92.942133
## iter 10 value 18.558935
## iter 20 value 9.135916
## iter 30 value 7.594404
## iter 40 value 5.998984
## iter 50 value 4.516121
## iter 60 value 3.997783
## iter 70 value 3.901226
## iter 80 value 3.869598
## iter 90 value 3.838507
## iter 100 value 3.768804
## final value 3.768804
## stopped after 100 iterations
## # weights: 39
## initial value 95.099086
## iter 10 value 56.038815
## iter 20 value 46.996571
## iter 30 value 46.541976
## iter 40 value 45.899882
## iter 50 value 44.852816
## iter 60 value 44.247409
## iter 70 value 44.080407
## final value 44.079338
## converged
## # weights: 109
## initial value 114.807598
## iter 10 value 47.933184
## iter 20 value 30.095918
## iter 30 value 19.618417
## iter 40 value 14.888073
## iter 50 value 13.973251
## iter 60 value 13.450400
## iter 70 value 11.919241
## iter 80 value 11.834870
## iter 90 value 11.832882
## final value 11.832580
## converged
## # weights: 179
## initial value 103.989063
## iter 10 value 48.679498
## iter 20 value 16.529536
## iter 30 value 12.114281
## iter 40 value 11.247450
## iter 50 value 11.104977
## iter 60 value 11.098787
## iter 70 value 11.098566
## final value 11.098552
## converged
## # weights: 39
## initial value 84.167025
## iter 10 value 55.517782
## iter 20 value 52.204104
## iter 30 value 51.519064
## iter 40 value 50.648501
## iter 50 value 48.581665
## iter 60 value 48.538721
## iter 70 value 48.029915
## iter 80 value 47.688211
## iter 90 value 47.665114
## iter 100 value 47.584245
## final value 47.584245
## stopped after 100 iterations
## # weights: 109
## initial value 111.186645
## iter 10 value 23.941476
## iter 20 value 7.791402
## iter 30 value 5.995794
## iter 40 value 0.497591
## iter 50 value 0.340925
## iter 60 value 0.331554
## iter 70 value 0.320785
## iter 80 value 0.308540
## iter 90 value 0.301206
## iter 100 value 0.296787
## final value 0.296787
## stopped after 100 iterations
## # weights: 179
## initial value 79.230253
## iter 10 value 16.340692
## iter 20 value 1.058126
## iter 30 value 0.170543
## iter 40 value 0.151643
## iter 50 value 0.127515
## iter 60 value 0.112913
## iter 70 value 0.104021
## iter 80 value 0.099967
## iter 90 value 0.096261
## iter 100 value 0.091898
## final value 0.091898
## stopped after 100 iterations
## # weights: 39
## initial value 84.115611
## iter 10 value 51.888072
## iter 20 value 42.919513
## iter 30 value 40.510190
## iter 40 value 40.462647
## iter 50 value 40.461453
## final value 40.461439
## converged
## # weights: 109
## initial value 88.593368
## iter 10 value 43.543549
## iter 20 value 6.637161
## iter 30 value 0.038279
## iter 40 value 0.008543
## iter 50 value 0.001907
## iter 60 value 0.000241
## final value 0.000093
## converged
## # weights: 179
## initial value 84.414587
## iter 10 value 18.519580
## iter 20 value 3.546465
## iter 30 value 0.151060
## iter 40 value 0.002930
## iter 50 value 0.000135
## iter 50 value 0.000082
## iter 50 value 0.000082
## final value 0.000082
## converged
## # weights: 39
## initial value 90.958227
## iter 10 value 56.066105
## iter 20 value 48.316351
## iter 30 value 48.031360
## iter 40 value 46.327371
## iter 50 value 44.724163
## iter 60 value 44.620837
## final value 44.620744
## converged
## # weights: 109
## initial value 86.736202
## iter 10 value 34.612246
## iter 20 value 16.580279
## iter 30 value 13.804108
## iter 40 value 13.607868
## iter 50 value 13.260563
## iter 60 value 13.154417
## iter 70 value 13.025307
## iter 80 value 12.966463
## final value 12.966462
## converged
## # weights: 179
## initial value 101.403698
## iter 10 value 35.958371
## iter 20 value 15.177125
## iter 30 value 12.051414
## iter 40 value 10.679620
## iter 50 value 10.343655
## iter 60 value 10.304440
## iter 70 value 10.299128
## iter 80 value 10.298832
## iter 90 value 10.298806
## iter 100 value 10.298803
## final value 10.298803
## stopped after 100 iterations
## # weights: 39
## initial value 85.541371
## iter 10 value 40.462490
## iter 20 value 39.144683
## iter 30 value 38.120839
## iter 40 value 36.371694
## iter 50 value 31.632393
## iter 60 value 31.271657
## iter 70 value 31.227020
## iter 80 value 31.172057
## iter 90 value 30.478786
## iter 100 value 29.343389
## final value 29.343389
## stopped after 100 iterations
## # weights: 109
## initial value 89.328189
## iter 10 value 16.856487
## iter 20 value 9.407713
## iter 30 value 7.715865
## iter 40 value 7.699714
## iter 50 value 7.070750
## iter 60 value 3.107886
## iter 70 value 0.279817
## iter 80 value 0.143693
## iter 90 value 0.134043
## iter 100 value 0.129457
## final value 0.129457
## stopped after 100 iterations
## # weights: 179
## initial value 94.432791
## iter 10 value 19.139524
## iter 20 value 2.816546
## iter 30 value 1.594628
## iter 40 value 0.224761
## iter 50 value 0.206273
## iter 60 value 0.185096
## iter 70 value 0.155346
## iter 80 value 0.125782
## iter 90 value 0.116220
## iter 100 value 0.098472
## final value 0.098472
## stopped after 100 iterations
## # weights: 39
## initial value 95.581378
## iter 10 value 59.749052
## iter 20 value 53.598882
## iter 30 value 52.162214
## iter 40 value 50.862310
## iter 50 value 46.575285
## iter 60 value 46.371992
## iter 70 value 46.184039
## iter 80 value 45.930161
## iter 90 value 45.876062
## iter 100 value 45.867832
## final value 45.867832
## stopped after 100 iterations
## # weights: 109
## initial value 89.923919
## iter 10 value 29.004092
## iter 20 value 21.413658
## iter 30 value 19.603756
## iter 40 value 18.642277
## iter 50 value 18.152229
## iter 60 value 18.092563
## iter 70 value 18.065668
## iter 80 value 12.138800
## iter 90 value 4.764959
## iter 100 value 0.345254
## final value 0.345254
## stopped after 100 iterations
## # weights: 179
## initial value 90.315614
## iter 10 value 40.845016
## iter 20 value 17.904211
## iter 30 value 15.699816
## iter 40 value 10.364004
## iter 50 value 0.917098
## iter 60 value 0.019451
## iter 70 value 0.002590
## iter 80 value 0.000530
## iter 90 value 0.000151
## iter 100 value 0.000130
## final value 0.000130
## stopped after 100 iterations
## # weights: 39
## initial value 85.169125
## iter 10 value 64.972884
## iter 20 value 52.467356
## iter 30 value 46.753811
## iter 40 value 45.058500
## iter 50 value 43.553622
## iter 60 value 41.378325
## iter 70 value 40.437582
## iter 80 value 40.419734
## final value 40.419730
## converged
## # weights: 109
## initial value 89.255698
## iter 10 value 30.551451
## iter 20 value 15.049853
## iter 30 value 13.823229
## iter 40 value 12.961858
## iter 50 value 11.828680
## iter 60 value 11.727350
## iter 70 value 11.724338
## final value 11.724324
## converged
## # weights: 179
## initial value 92.643328
## iter 10 value 32.356206
## iter 20 value 12.043873
## iter 30 value 9.610067
## iter 40 value 9.171488
## iter 50 value 9.127067
## iter 60 value 9.126000
## final value 9.125989
## converged
## # weights: 39
## initial value 86.436602
## iter 10 value 51.324144
## iter 20 value 41.189983
## iter 30 value 36.271056
## iter 40 value 23.473940
## iter 50 value 19.037496
## iter 60 value 14.684252
## iter 70 value 10.092664
## iter 80 value 7.391404
## iter 90 value 7.353136
## iter 100 value 7.284000
## final value 7.284000
## stopped after 100 iterations
## # weights: 109
## initial value 92.446436
## iter 10 value 7.748643
## iter 20 value 3.646915
## iter 30 value 3.250936
## iter 40 value 3.020993
## iter 50 value 1.715862
## iter 60 value 1.639589
## iter 70 value 1.633451
## iter 80 value 1.602800
## iter 90 value 1.596145
## iter 100 value 1.583612
## final value 1.583612
## stopped after 100 iterations
## # weights: 179
## initial value 84.105863
## iter 10 value 28.459374
## iter 20 value 3.942282
## iter 30 value 2.747349
## iter 40 value 2.726469
## iter 50 value 2.700101
## iter 60 value 2.680525
## iter 70 value 1.644747
## iter 80 value 1.597145
## iter 90 value 0.373498
## iter 100 value 0.213030
## final value 0.213030
## stopped after 100 iterations
## # weights: 39
## initial value 87.885213
## iter 10 value 44.213304
## iter 20 value 42.301055
## iter 30 value 41.199692
## iter 40 value 39.422634
## iter 50 value 39.165472
## iter 60 value 39.099620
## iter 70 value 39.098662
## iter 80 value 39.098118
## iter 90 value 39.092692
## iter 100 value 38.860468
## final value 38.860468
## stopped after 100 iterations
## # weights: 109
## initial value 80.616963
## iter 10 value 24.804407
## iter 20 value 19.850656
## iter 30 value 19.548855
## iter 40 value 19.477124
## iter 50 value 15.696901
## iter 60 value 7.474428
## iter 70 value 7.314817
## iter 80 value 7.278091
## iter 90 value 7.272003
## iter 100 value 3.395780
## final value 3.395780
## stopped after 100 iterations
## # weights: 179
## initial value 87.602008
## iter 10 value 20.302238
## iter 20 value 7.532904
## iter 30 value 3.263114
## iter 40 value 2.996990
## iter 50 value 1.476469
## iter 60 value 1.381468
## iter 70 value 0.030592
## iter 80 value 0.005738
## iter 90 value 0.002192
## iter 100 value 0.000936
## final value 0.000936
## stopped after 100 iterations
## # weights: 39
## initial value 91.305407
## iter 10 value 58.618654
## iter 20 value 53.080022
## iter 30 value 52.363335
## iter 40 value 51.065755
## iter 50 value 50.313106
## iter 60 value 49.461833
## iter 70 value 47.284185
## iter 80 value 47.221814
## final value 47.221812
## converged
## # weights: 109
## initial value 92.760705
## iter 10 value 27.576014
## iter 20 value 17.265204
## iter 30 value 13.778948
## iter 40 value 13.131824
## iter 50 value 13.130236
## final value 13.130235
## converged
## # weights: 179
## initial value 96.355526
## iter 10 value 33.826390
## iter 20 value 16.460663
## iter 30 value 10.778998
## iter 40 value 10.302176
## iter 50 value 10.172901
## iter 60 value 9.645958
## iter 70 value 9.617659
## iter 80 value 9.616849
## final value 9.616825
## converged
## # weights: 39
## initial value 87.074427
## iter 10 value 71.008343
## iter 20 value 56.164297
## iter 30 value 49.275055
## iter 40 value 47.825219
## iter 50 value 47.780336
## iter 60 value 47.751162
## iter 70 value 47.695417
## iter 80 value 47.658795
## iter 90 value 47.650970
## iter 100 value 47.566463
## final value 47.566463
## stopped after 100 iterations
## # weights: 109
## initial value 77.269200
## iter 10 value 35.617660
## iter 20 value 10.169319
## iter 30 value 5.360920
## iter 40 value 3.211887
## iter 50 value 2.393273
## iter 60 value 0.700683
## iter 70 value 0.425853
## iter 80 value 0.359251
## iter 90 value 0.335872
## iter 100 value 0.320847
## final value 0.320847
## stopped after 100 iterations
## # weights: 179
## initial value 79.680889
## iter 10 value 3.807592
## iter 20 value 0.126699
## iter 30 value 0.090285
## iter 40 value 0.083949
## iter 50 value 0.077477
## iter 60 value 0.073347
## iter 70 value 0.064331
## iter 80 value 0.058673
## iter 90 value 0.056376
## iter 100 value 0.054383
## final value 0.054383
## stopped after 100 iterations
## # weights: 39
## initial value 88.012074
## iter 10 value 71.405366
## iter 20 value 56.100726
## iter 30 value 56.076457
## iter 40 value 55.954540
## iter 50 value 55.646566
## iter 60 value 55.507305
## iter 70 value 55.458371
## iter 80 value 48.068367
## iter 90 value 44.233638
## iter 100 value 43.595158
## final value 43.595158
## stopped after 100 iterations
## # weights: 109
## initial value 81.471815
## iter 10 value 36.050582
## iter 20 value 15.036087
## iter 30 value 10.437203
## iter 40 value 0.553493
## iter 50 value 0.013375
## iter 60 value 0.003862
## iter 70 value 0.000989
## iter 80 value 0.000699
## iter 90 value 0.000587
## iter 100 value 0.000207
## final value 0.000207
## stopped after 100 iterations
## # weights: 179
## initial value 109.870317
## iter 10 value 7.851044
## iter 20 value 2.072646
## iter 30 value 1.914825
## iter 40 value 1.909850
## iter 50 value 1.909558
## final value 1.909545
## converged
## # weights: 39
## initial value 82.532556
## iter 10 value 50.471275
## iter 20 value 48.589357
## iter 30 value 46.680328
## iter 40 value 43.489034
## iter 50 value 43.372329
## final value 43.372243
## converged
## # weights: 109
## initial value 83.301827
## iter 10 value 50.239755
## iter 20 value 40.670091
## iter 30 value 31.821810
## iter 40 value 24.198455
## iter 50 value 15.692491
## iter 60 value 14.007769
## iter 70 value 13.350583
## iter 80 value 13.095102
## iter 90 value 13.067776
## final value 13.067539
## converged
## # weights: 179
## initial value 122.642259
## iter 10 value 34.125404
## iter 20 value 15.284064
## iter 30 value 11.129062
## iter 40 value 10.331692
## iter 50 value 9.949609
## iter 60 value 9.896971
## iter 70 value 9.896053
## iter 80 value 9.895976
## final value 9.895975
## converged
## # weights: 39
## initial value 84.988710
## iter 10 value 53.414109
## iter 20 value 48.781218
## iter 30 value 46.450066
## iter 40 value 45.855967
## iter 50 value 45.504685
## iter 60 value 44.001230
## iter 70 value 43.582510
## iter 80 value 43.501098
## iter 90 value 43.483141
## iter 100 value 42.489191
## final value 42.489191
## stopped after 100 iterations
## # weights: 109
## initial value 78.544943
## iter 10 value 34.269147
## iter 20 value 9.851332
## iter 30 value 7.574756
## iter 40 value 6.818164
## iter 50 value 6.345905
## iter 60 value 6.322714
## iter 70 value 6.295090
## iter 80 value 6.154939
## iter 90 value 5.614795
## iter 100 value 5.447095
## final value 5.447095
## stopped after 100 iterations
## # weights: 179
## initial value 95.661557
## iter 10 value 39.354820
## iter 20 value 12.037084
## iter 30 value 7.005599
## iter 40 value 0.186446
## iter 50 value 0.146341
## iter 60 value 0.135677
## iter 70 value 0.130545
## iter 80 value 0.114449
## iter 90 value 0.108235
## iter 100 value 0.101265
## final value 0.101265
## stopped after 100 iterations
## # weights: 39
## initial value 84.890458
## iter 10 value 56.609915
## iter 20 value 44.507153
## iter 30 value 35.914186
## iter 40 value 30.905717
## iter 50 value 26.887579
## iter 60 value 26.560884
## iter 70 value 25.304821
## iter 80 value 24.791798
## iter 90 value 24.715340
## iter 100 value 24.710278
## final value 24.710278
## stopped after 100 iterations
## # weights: 109
## initial value 99.098165
## iter 10 value 28.786201
## iter 20 value 7.929812
## iter 30 value 0.031978
## iter 40 value 0.006280
## iter 50 value 0.002113
## final value 0.000012
## converged
## # weights: 179
## initial value 115.439256
## iter 10 value 7.128630
## iter 20 value 2.070233
## iter 30 value 1.945668
## iter 40 value 1.921497
## iter 50 value 1.678276
## iter 60 value 0.391100
## iter 70 value 0.076208
## iter 80 value 0.029488
## iter 90 value 0.010619
## iter 100 value 0.003847
## final value 0.003847
## stopped after 100 iterations
## # weights: 39
## initial value 103.705680
## iter 10 value 53.211195
## iter 20 value 47.834961
## iter 30 value 44.319229
## iter 40 value 41.892499
## iter 50 value 38.594866
## iter 60 value 37.469823
## iter 70 value 37.362836
## iter 70 value 37.362835
## iter 70 value 37.362835
## final value 37.362835
## converged
## # weights: 109
## initial value 80.405311
## iter 10 value 31.917459
## iter 20 value 16.169677
## iter 30 value 11.327703
## iter 40 value 10.814074
## iter 50 value 10.778966
## iter 60 value 10.778060
## final value 10.778056
## converged
## # weights: 179
## initial value 84.189209
## iter 10 value 26.108640
## iter 20 value 12.013497
## iter 30 value 9.373290
## iter 40 value 8.958735
## iter 50 value 8.943334
## iter 60 value 8.943022
## final value 8.943015
## converged
## # weights: 39
## initial value 84.540267
## iter 10 value 44.420054
## iter 20 value 41.305691
## iter 30 value 31.367397
## iter 40 value 31.061725
## iter 50 value 31.059840
## iter 60 value 31.056806
## iter 70 value 31.055420
## iter 80 value 31.053729
## iter 90 value 31.049046
## iter 100 value 29.998950
## final value 29.998950
## stopped after 100 iterations
## # weights: 109
## initial value 81.228345
## iter 10 value 24.272873
## iter 20 value 7.497484
## iter 30 value 6.753635
## iter 40 value 6.483649
## iter 50 value 6.352889
## iter 60 value 6.264559
## iter 70 value 6.195808
## iter 80 value 3.586113
## iter 90 value 3.487459
## iter 100 value 3.435916
## final value 3.435916
## stopped after 100 iterations
## # weights: 179
## initial value 113.126314
## iter 10 value 19.505697
## iter 20 value 8.627369
## iter 30 value 8.443156
## iter 40 value 6.717805
## iter 50 value 4.099864
## iter 60 value 0.224279
## iter 70 value 0.166034
## iter 80 value 0.153499
## iter 90 value 0.145463
## iter 100 value 0.122348
## final value 0.122348
## stopped after 100 iterations
## # weights: 39
## initial value 86.097523
## iter 10 value 49.936030
## iter 20 value 43.890027
## iter 30 value 42.729551
## iter 40 value 42.591851
## iter 50 value 42.572274
## iter 60 value 42.569087
## iter 70 value 42.568819
## final value 42.568312
## converged
## # weights: 109
## initial value 84.320196
## iter 10 value 23.041738
## iter 20 value 7.016379
## iter 30 value 4.522576
## iter 40 value 4.498505
## iter 50 value 4.257135
## iter 60 value 4.187924
## iter 70 value 4.187898
## final value 4.187898
## converged
## # weights: 179
## initial value 119.704908
## iter 10 value 8.974781
## iter 20 value 3.231054
## iter 30 value 2.202606
## iter 40 value 0.317631
## iter 50 value 0.119630
## iter 60 value 0.017991
## iter 70 value 0.001912
## iter 80 value 0.000749
## iter 90 value 0.000415
## iter 100 value 0.000260
## final value 0.000260
## stopped after 100 iterations
## # weights: 39
## initial value 96.359105
## iter 10 value 64.894576
## iter 20 value 51.891959
## iter 30 value 48.754777
## iter 40 value 47.959488
## final value 47.954624
## converged
## # weights: 109
## initial value 97.005897
## iter 10 value 33.293174
## iter 20 value 18.525335
## iter 30 value 14.925314
## iter 40 value 14.565280
## iter 50 value 14.506521
## iter 60 value 14.483830
## iter 70 value 14.464465
## iter 80 value 14.463810
## iter 90 value 14.463780
## iter 90 value 14.463780
## iter 90 value 14.463780
## final value 14.463780
## converged
## # weights: 179
## initial value 94.403785
## iter 10 value 32.147049
## iter 20 value 17.150787
## iter 30 value 11.956276
## iter 40 value 10.328922
## iter 50 value 10.071316
## iter 60 value 10.049796
## iter 70 value 10.049173
## iter 80 value 10.049164
## iter 80 value 10.049164
## iter 80 value 10.049164
## final value 10.049164
## converged
## # weights: 39
## initial value 90.472972
## iter 10 value 48.666843
## iter 20 value 43.194239
## iter 30 value 38.995691
## iter 40 value 35.596253
## iter 50 value 35.483457
## iter 60 value 35.475603
## iter 70 value 35.466387
## iter 80 value 35.464677
## iter 90 value 35.416972
## iter 100 value 35.117280
## final value 35.117280
## stopped after 100 iterations
## # weights: 109
## initial value 89.592870
## iter 10 value 31.275267
## iter 20 value 18.922981
## iter 30 value 17.684342
## iter 40 value 16.672119
## iter 50 value 16.448323
## iter 60 value 15.837667
## iter 70 value 15.764839
## iter 80 value 14.154531
## iter 90 value 12.340381
## iter 100 value 12.326574
## final value 12.326574
## stopped after 100 iterations
## # weights: 179
## initial value 84.851448
## iter 10 value 23.438295
## iter 20 value 5.866080
## iter 30 value 4.022276
## iter 40 value 0.939609
## iter 50 value 0.441182
## iter 60 value 0.306766
## iter 70 value 0.301015
## iter 80 value 0.278531
## iter 90 value 0.258674
## iter 100 value 0.233688
## final value 0.233688
## stopped after 100 iterations
## # weights: 39
## initial value 82.020546
## iter 10 value 64.805221
## iter 20 value 41.690696
## iter 30 value 40.074506
## iter 40 value 40.072438
## final value 40.072432
## converged
## # weights: 109
## initial value 79.821808
## iter 10 value 27.937265
## iter 20 value 1.938374
## iter 30 value 0.198284
## iter 40 value 0.013956
## iter 50 value 0.004000
## iter 60 value 0.002540
## iter 70 value 0.000539
## iter 80 value 0.000221
## final value 0.000096
## converged
## # weights: 179
## initial value 85.287590
## iter 10 value 2.195586
## iter 20 value 0.019398
## iter 30 value 0.001846
## final value 0.000095
## converged
## # weights: 39
## initial value 84.317257
## iter 10 value 57.682888
## iter 20 value 50.444700
## iter 30 value 45.846096
## iter 40 value 45.692229
## final value 45.691802
## converged
## # weights: 109
## initial value 83.435358
## iter 10 value 31.643228
## iter 20 value 16.735840
## iter 30 value 13.280159
## iter 40 value 11.678191
## iter 50 value 11.580044
## iter 60 value 11.576538
## iter 70 value 11.576507
## iter 70 value 11.576507
## iter 70 value 11.576507
## final value 11.576507
## converged
## # weights: 179
## initial value 84.465331
## iter 10 value 36.219337
## iter 20 value 19.878112
## iter 30 value 11.322392
## iter 40 value 9.252890
## iter 50 value 8.599707
## iter 60 value 8.567128
## iter 70 value 8.565120
## iter 80 value 8.564941
## final value 8.564932
## converged
## # weights: 39
## initial value 89.738634
## iter 10 value 58.493085
## iter 20 value 57.203495
## iter 30 value 56.970584
## iter 40 value 56.953117
## iter 50 value 51.550271
## iter 60 value 50.715648
## iter 70 value 50.710615
## iter 80 value 50.575481
## iter 90 value 47.142865
## iter 100 value 47.131192
## final value 47.131192
## stopped after 100 iterations
## # weights: 109
## initial value 96.567744
## iter 10 value 37.915738
## iter 20 value 0.404338
## iter 30 value 0.280086
## iter 40 value 0.202904
## iter 50 value 0.157915
## iter 60 value 0.135142
## iter 70 value 0.128417
## iter 80 value 0.121374
## iter 90 value 0.112813
## iter 100 value 0.105724
## final value 0.105724
## stopped after 100 iterations
## # weights: 179
## initial value 119.659448
## iter 10 value 37.063911
## iter 20 value 17.493135
## iter 30 value 7.844024
## iter 40 value 7.159135
## iter 50 value 7.141693
## iter 60 value 7.130486
## iter 70 value 5.520374
## iter 80 value 5.502448
## iter 90 value 5.161716
## iter 100 value 0.189676
## final value 0.189676
## stopped after 100 iterations
## # weights: 179
## initial value 91.203441
## iter 10 value 27.811912
## iter 20 value 16.551713
## iter 30 value 12.649276
## iter 40 value 11.288908
## iter 50 value 10.775554
## iter 60 value 10.626294
## iter 70 value 10.618773
## iter 80 value 10.509283
## iter 90 value 10.451275
## iter 100 value 10.448875
## final value 10.448875
## stopped after 100 iterationsnnpred_model2 <- predict(neuralnet_model2, newdata= data_norm[-samp,])#viewing confusion matrix
confusionMatrix(nnpred_model2, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 5 1 0 0
## HER2.enriched 0 1 0 0
## Luminal.A 0 0 6 2
## Luminal.B 0 1 0 5
##
## Overall Statistics
##
## Accuracy : 0.8095
## 95% CI : (0.5809, 0.9455)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 1.026e-05
##
## Kappa : 0.7358
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 1.0000 0.33333 1.0000
## Specificity 0.9375 1.00000 0.8667
## Pos Pred Value 0.8333 1.00000 0.7500
## Neg Pred Value 1.0000 0.90000 1.0000
## Precision 0.8333 1.00000 0.7500
## Recall 1.0000 0.33333 1.0000
## F1 0.9091 0.50000 0.8571
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.2381 0.04762 0.2857
## Detection Prevalence 0.2857 0.04762 0.3810
## Balanced Accuracy 0.9688 0.66667 0.9333
## Class: Luminal.B
## Sensitivity 0.7143
## Specificity 0.9286
## Pos Pred Value 0.8333
## Neg Pred Value 0.8667
## Precision 0.8333
## Recall 0.7143
## F1 0.7692
## Prevalence 0.3333
## Detection Rate 0.2381
## Detection Prevalence 0.2857
## Balanced Accuracy 0.8214Model evaluation for NN
Accuracy : The overall model accuracy of Neural network model is 81%
Precision, Recall, F-1 : The Precision,Recall,F1 for Basal.like class is 0.8333, 1.0000 , 0.9091 The Precision,Recall,F1 for HER2.enriched class is 1.00000, 0.33333 , 0.50000 The Precision,Recall,F1 for Luminal.A class is 0.7500, 1.0000, 0.8571 The Precision,Recall,F1 for Luminal.B class is 0.8333, 0.7143,0.7692
Sensitivity and Specificity The Sensitivity and Specificity for Basal.like class is 1.0000 and 0.9375 The Sensitivity and Specificity for HER2.enriched class is 0.33333 and 1.00000 The Sensitivity and Specificity for Luminal.A is 1.0000 and 0.8667 The Sensitivity and Specificity for Luminal.B class is 0.7143 and 0.9286
The Kappa statistic: The kappa value for this model is 0.7358 which states that it is a good agreement.
Macro-averaged Metrics : The per-class metrics can be averaged over all the classes resulting in macro-averaged precision, recall and F-1.
# macro-averaged precision
precision_nn <- c(0.8333,1.00000, 0.7500, 0.8333)
macro_precision_nn <- mean(precision_nn)
# macro-averaged recall
recall_nn <- c(1.0000, 0.33333, 1.0000, 0.7143)
macro_recall_nn<- mean(recall_nn)
# macro-averaged F-1
F1_nn<- c(0.9091,0.50000,0.8571,0.7692)
macroF1_nn <- mean(F1_nn)
macro_average_nn <-data.frame(macro_precision_nn, macro_recall_nn, macroF1_nn)
macro_average_nnAUC
nn_auc <- multiclass.roc(test$PAM50.mRNA, as.ordered(nnpred_model2))
auc(nn_auc)## Multi-class area under the curve: 0.8571Name_metrics <- c("Accuracy", "Precision", "Recall", "F-1", "AUC", "Kappa")
values_nn <- c(0.8095 , 0.85415, 0.7619075, 0.75885,0.8571,0.7358 )
metrics_nn <- data.frame(Name_metrics, values_nn)
print (metrics_nn)## Name_metrics values_nn
## 1 Accuracy 0.8095000
## 2 Precision 0.8541500
## 3 Recall 0.7619075
## 4 F-1 0.7588500
## 5 AUC 0.8571000
## 6 Kappa 0.7358000Naive Baye’s
The Naive Bayes Algorithm is a classifier based on applying Bayes theorem with independent assumptions between features. Meaning that all features in the data set are equally important and independent of one another. Bayesian probability Is rooted in the theory that the likelihood of an event should be based on the evidence across multiple trials. Naïve Bayes uses probabilities to classify groups based on prior probability. One advantage is that Naïve Bayes works with mixed data: nominal, continuous and ordinal variables. Naïve Bayes is fast and effective, handles missing and noisy data well, and requires few records for training and can also work well with large records. Disadvantages of Naïve Bayes is that it assumes that all the data predictors are independent when in data is far from this faulty assumption. Also, estimated probabilities are less reliable than predicted classes.
nb_model3 <- naive_bayes(PAM50.mRNA ~ ., data = train, usekernel = T)
nbpred_model3 <- predict(nb_model3, newdata= data_norm[-samp,])
#viewing confusion matrix
confusionMatrix(nbpred_model3 , factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 3 1 0 0
## HER2.enriched 1 2 0 0
## Luminal.A 1 0 5 1
## Luminal.B 0 0 1 6
##
## Overall Statistics
##
## Accuracy : 0.7619
## 95% CI : (0.5283, 0.9178)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 7.251e-05
##
## Kappa : 0.6729
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 0.6000 0.66667 0.8333
## Specificity 0.9375 0.94444 0.8667
## Pos Pred Value 0.7500 0.66667 0.7143
## Neg Pred Value 0.8824 0.94444 0.9286
## Precision 0.7500 0.66667 0.7143
## Recall 0.6000 0.66667 0.8333
## F1 0.6667 0.66667 0.7692
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.1429 0.09524 0.2381
## Detection Prevalence 0.1905 0.14286 0.3333
## Balanced Accuracy 0.7688 0.80556 0.8500
## Class: Luminal.B
## Sensitivity 0.8571
## Specificity 0.9286
## Pos Pred Value 0.8571
## Neg Pred Value 0.9286
## Precision 0.8571
## Recall 0.8571
## F1 0.8571
## Prevalence 0.3333
## Detection Rate 0.2857
## Detection Prevalence 0.3333
## Balanced Accuracy 0.8929Model evaluation for NB:
- Accuracy : The overall model accuracy of Naive Baye’s model is 76. 2%
- Precision,Recall,F1 : The Precision,Recall,F1 for Basal.like class
is 0.7500, 0.60000, 0.6667 The Precision,Recall,F1 for HER2.enriched
class is 0.6667, 0.6667, 0.6667 The Precision,Recall,F1 for Luminal.A
class is 0.7143, 0.8333, 0.71692 The Precision,Recall,F1 for Luminal.B
class is 0.8571, 0.8571, 0.8571
- Sensitivity and Specificity The Sensitivity and Specificity for Basal.like class is 0.60000 and 0.9375 The Sensitivity and Specificity for HER2.enriched class is 0.66667 and 0.94444 The Sensitivity and Specificity for Luminal.A is 0.8333 and 0.8667 The Sensitivity and Specificity for Luminal.B class is 0.8571 and 0.9286
- The Kappa statistic: The kappa value for this model is 0.6729 which states that it is a good agreement.
Macro-averaged Metrics : The per-class metrics can be averaged over all the classes resulting in macro-averaged precision, recall and F-1.
# macro-averaged precision
precision_nb <- c(0.7500,0.66667,0.7143,0.8571)
macro_precision_nb <- mean(precision_nb)
# macro-averaged recall
recall_nb <- c(0.6000,0.66667,0.8333,0.8571)
macro_recall_nb<- mean(recall_nb)
# macro-averaged F-1
F1_nb<- c(0.6667,0.66667,0.7692,0.8571)
macroF1_nb <- mean(F1_nb)
macro_average_nb <-data.frame(macro_precision_nb, macro_recall_nb, macroF1_nb)
macro_average_nbAUC
nb_auc <- multiclass.roc(test$PAM50.mRNA, as.ordered(nbpred_model3))
auc(nb_auc)## Multi-class area under the curve: 0.8857Name_metrics <- c("Accuracy", "Precision", "Recall", "F-1", "AUC", "Kappa")
values_nb <- c(0.7619, 0.7470175, 0.7392675,0.7399175, 0.8857, 0.6729 )
metrics_nb <- data.frame(Name_metrics, values_nb)
print (metrics_nb)## Name_metrics values_nb
## 1 Accuracy 0.7619000
## 2 Precision 0.7470175
## 3 Recall 0.7392675
## 4 F-1 0.7399175
## 5 AUC 0.8857000
## 6 Kappa 0.6729000Random Forest
Random Forest is an ensemble of decision trees. It builds and combines multiple decision trees to get more accurate predictions. It’s a non-linear classification algorithm. Each decision tree model is used when employed on its own. It works well with the multiclass classification.
# Fitting Random Forest to the train dataset
set.seed(120) # Setting seed
classifier_RF = randomForest(x = train[,1:30],
y = train$PAM50.mRNA,
ntree = 500)
RF_pred <- predict(classifier_RF, newdata = test)
confusionMatrix(RF_pred, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 4 1 1 1
## HER2.enriched 0 1 0 0
## Luminal.A 1 0 4 2
## Luminal.B 0 1 1 4
##
## Overall Statistics
##
## Accuracy : 0.619
## 95% CI : (0.3844, 0.8189)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 0.006807
##
## Kappa : 0.4734
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 0.8000 0.33333 0.6667
## Specificity 0.8125 1.00000 0.8000
## Pos Pred Value 0.5714 1.00000 0.5714
## Neg Pred Value 0.9286 0.90000 0.8571
## Precision 0.5714 1.00000 0.5714
## Recall 0.8000 0.33333 0.6667
## F1 0.6667 0.50000 0.6154
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.1905 0.04762 0.1905
## Detection Prevalence 0.3333 0.04762 0.3333
## Balanced Accuracy 0.8063 0.66667 0.7333
## Class: Luminal.B
## Sensitivity 0.5714
## Specificity 0.8571
## Pos Pred Value 0.6667
## Neg Pred Value 0.8000
## Precision 0.6667
## Recall 0.5714
## F1 0.6154
## Prevalence 0.3333
## Detection Rate 0.1905
## Detection Prevalence 0.2857
## Balanced Accuracy 0.7143# Plotting model
plot(classifier_RF)
# Importance plot
importance(classifier_RF)## MeanDecreaseGini
## NP_038479 0.8805231
## NP_001258898 1.0524888
## NP_542785 1.8547260
## NP_002005 1.8352126
## XP_003846537 1.6043369
## NP_660208 1.3260767
## NP_004439 0.5102471
## NP_004388 1.0973934
## NP_001229372 0.5588631
## NP_005130 1.2257370
## NP_004243 3.3422886
## NP_003144 2.6735076
## NP_001171551 3.1100743
## NP_072096 0.7202323
## NP_005484 1.6973190
## NP_061170 1.1520539
## NP_653304 1.9111497
## NP_653081 0.4687590
## NP_057164 0.9427079
## NP_004453 2.4038736
## NP_003212 1.6859924
## NP_060866 1.4245630
## NP_001160163 1.2619327
## NP_003875 1.4044818
## NP_002677 0.4121577
## NP_001135891 0.8641273
## NP_004522 1.6539665
## NP_003767 1.2076640
## NP_848597 1.1409587
## NP_005970 1.4146192# Variable importance plot
varImpPlot(classifier_RF)
# Model evaluation for RF: 1. Accuracy : The overall model accuracy of
model is 61.9% 2. Precision,Recall,F1 : The Precision,Recall,F1 for
Basal.like class is 0.5714 , 0.8000, 0.6667 The Precision,Recall,F1 for
HER2.enriched class is 1.00000, 0.33333, 0.50000 The Precision,Recall,F1
for Luminal.A class is 0.5714, 0.6667, 0.6154 The Precision,Recall,F1
for Luminal.B class is 0.6667, 0.5714, 0.6154
3. Sensitivity and Specificity The Sensitivity and Specificity for
Basal.like class is 0.80000 and 0.8125 The Sensitivity and Specificity
for HER2.enriched class is 0.333333 and 1.00000 The Sensitivity and
Specificity for Luminal.A is 0.6667 and 0.8000 The Sensitivity and
Specificity for Luminal.B class is 0.5714 and 0.8571 4. The Kappa
statistic: The kappa value for this model is 0.473 which states that it
is a good agreement.
Macro-averaged Metrics : The per-class metrics can be averaged over all the classes resulting in macro-averaged precision, recall and F-1.
# macro-averaged precision
precision_rf<- c(0.5714,1.00000, 0.5714, 0.6667)
macro_precision_rf <- mean(precision_rf)
# macro-averaged recall
recall_rf <- c(0.8000, 0.33333,0.6667,0.5714)
macro_recall_rf<- mean(recall_rf)
# macro-averaged F-1
F1_rf<- c(0.6667, 0.50000, 0.6154,0.6154)
macroF1_rf <- mean(F1_rf)
macro_average_rf <-data.frame(macro_precision_rf, macro_recall_rf, macroF1_rf)
macro_average_rfAUC
rf_auc <- multiclass.roc(test$PAM50.mRNA, as.ordered(RF_pred))
auc(rf_auc)## Multi-class area under the curve: 0.7388Name_metrics <- c("Accuracy", "Precision", "Recall", "F-1", "AUC", "Kappa")
values_rf <- c(0.619, 0.702375, 0.5928575, 0.599375, 0.7388, 0.4734)
metrics_rf <- data.frame(Name_metrics, values_rf)
print (metrics_rf)## Name_metrics values_rf
## 1 Accuracy 0.6190000
## 2 Precision 0.7023750
## 3 Recall 0.5928575
## 4 F-1 0.5993750
## 5 AUC 0.7388000
## 6 Kappa 0.4734000Comparing models
By comparing accuracy, precision, recall, f-1, AUC and kappa , Both SVM and the Neural Network model works best for the given datset have approximate same accuracy, precision, recall, f-1, AUC and kappa
#SVM
print(metrics_svm)## Name_metrics values_svm
## 1 Accuracy 0.8095000
## 2 Precision 0.8428500
## 3 Recall 0.8095175
## 4 F-1 0.8057750
## 5 AUC 0.7897000
## 6 Kappa 0.7399000#NN
print(metrics_nn)## Name_metrics values_nn
## 1 Accuracy 0.8095000
## 2 Precision 0.8541500
## 3 Recall 0.7619075
## 4 F-1 0.7588500
## 5 AUC 0.8571000
## 6 Kappa 0.7358000#NB
print(metrics_nb)## Name_metrics values_nb
## 1 Accuracy 0.7619000
## 2 Precision 0.7470175
## 3 Recall 0.7392675
## 4 F-1 0.7399175
## 5 AUC 0.8857000
## 6 Kappa 0.6729000#RF
print(metrics_rf)## Name_metrics values_rf
## 1 Accuracy 0.6190000
## 2 Precision 0.7023750
## 3 Recall 0.5928575
## 4 F-1 0.5993750
## 5 AUC 0.7388000
## 6 Kappa 0.4734000Though the SVM and neural network model achieved an accuracy of 81%, the precision, recall, AUC, kappa, and f-1 scores of neural network model are higher than SVM
Evaluation with k-fold cross-validation
- k-Fold Cross Validation is done for the whole dataset
- I have used k = 10 which means 10 folds take place along with 10 repetitions
- For testing the data, I have used 3 models to test the k-fold CV
- Accuracy of each model is printed and based on the observation average accuracy is around 75-80%
K-fold Cross Validation
#Creating a train function for cross validation #We use k = 10 folds with repeated validation = 10
fitControl <- trainControl(## 10-fold CV
method = "repeatedcv",
number = 10,repeats = 10,savePredictions = TRUE, summaryFunction = multiClassSummary)
## SVM
svm_fit <- train(PAM50.mRNA ~ ., data=data_norm, trControl= fitControl, method="svmLinear")
## NN
nn_fit <- train(PAM50.mRNA ~., data= data_norm,
method = "nnet",
trControl = fitControl)## # weights: 39
## initial value 104.299245
## iter 10 value 79.995263
## iter 20 value 68.794379
## iter 30 value 60.381853
## iter 40 value 59.388098
## iter 50 value 59.379352
## final value 59.379337
## converged
## # weights: 109
## initial value 116.362540
## iter 10 value 38.703189
## iter 20 value 27.949090
## iter 30 value 17.701280
## iter 40 value 14.439067
## iter 50 value 12.383946
## iter 60 value 6.798820
## iter 70 value 4.387518
## iter 80 value 4.103604
## iter 90 value 4.059599
## iter 100 value 4.000446
## final value 4.000446
## stopped after 100 iterations
## # weights: 179
## initial value 113.501953
## iter 10 value 25.731497
## iter 20 value 1.147697
## iter 30 value 0.007144
## iter 40 value 0.000410
## final value 0.000094
## converged
## # weights: 39
## initial value 106.824209
## iter 10 value 83.502515
## iter 20 value 68.645068
## iter 30 value 65.457292
## iter 40 value 61.513075
## iter 50 value 58.252477
## iter 60 value 57.896911
## final value 57.894931
## converged
## # weights: 109
## initial value 116.266962
## iter 10 value 51.596366
## iter 20 value 36.012333
## iter 30 value 21.950493
## iter 40 value 17.872911
## iter 50 value 17.036263
## iter 60 value 16.922137
## iter 70 value 16.914098
## final value 16.913988
## converged
## # weights: 179
## initial value 122.764369
## iter 10 value 38.320919
## iter 20 value 20.761799
## iter 30 value 14.930962
## iter 40 value 13.658736
## iter 50 value 12.792821
## iter 60 value 12.222951
## iter 70 value 12.160783
## iter 80 value 12.155308
## iter 90 value 12.155177
## final value 12.155175
## converged
## # weights: 39
## initial value 102.999903
## iter 10 value 71.735883
## iter 20 value 70.410970
## iter 30 value 70.294498
## iter 40 value 67.538440
## iter 50 value 66.281131
## iter 60 value 66.192470
## iter 70 value 66.108587
## iter 80 value 63.079601
## iter 90 value 61.633491
## iter 100 value 60.638393
## final value 60.638393
## stopped after 100 iterations
## # weights: 109
## initial value 99.973885
## iter 10 value 46.734583
## iter 20 value 31.972769
## iter 30 value 30.881697
## iter 40 value 28.947185
## iter 50 value 27.917324
## iter 60 value 22.957983
## iter 70 value 22.055213
## iter 80 value 18.966393
## iter 90 value 18.163339
## iter 100 value 18.117185
## final value 18.117185
## stopped after 100 iterations
## # weights: 179
## initial value 124.898739
## iter 10 value 26.580909
## iter 20 value 12.932771
## iter 30 value 8.485489
## iter 40 value 5.668525
## iter 50 value 2.874174
## iter 60 value 2.248487
## iter 70 value 2.188451
## iter 80 value 2.157638
## iter 90 value 2.128182
## iter 100 value 1.592795
## final value 1.592795
## stopped after 100 iterations
## # weights: 39
## initial value 107.534552
## iter 10 value 77.408126
## iter 20 value 62.583232
## iter 30 value 62.127657
## iter 40 value 61.841436
## iter 50 value 61.220111
## iter 60 value 59.870007
## iter 70 value 57.536401
## iter 80 value 57.193123
## iter 90 value 57.079719
## iter 100 value 56.830559
## final value 56.830559
## stopped after 100 iterations
## # weights: 109
## initial value 95.978593
## iter 10 value 27.730307
## iter 20 value 11.535128
## iter 30 value 9.429867
## iter 40 value 6.068945
## iter 50 value 2.904714
## iter 60 value 0.581600
## iter 70 value 0.258821
## iter 80 value 0.119119
## iter 90 value 0.050529
## iter 100 value 0.015378
## final value 0.015378
## stopped after 100 iterations
## # weights: 179
## initial value 110.151479
## iter 10 value 29.390762
## iter 20 value 12.489817
## iter 30 value 11.199423
## iter 40 value 11.006596
## iter 50 value 9.140870
## iter 60 value 8.865245
## iter 70 value 8.322997
## iter 80 value 7.494911
## iter 90 value 6.679223
## iter 100 value 6.612051
## final value 6.612051
## stopped after 100 iterations
## # weights: 39
## initial value 99.289685
## iter 10 value 74.506952
## iter 20 value 60.928284
## iter 30 value 56.968706
## iter 40 value 54.908474
## iter 50 value 54.781351
## final value 54.781138
## converged
## # weights: 109
## initial value 137.357072
## iter 10 value 46.207196
## iter 20 value 25.596747
## iter 30 value 17.876964
## iter 40 value 15.953225
## iter 50 value 15.860954
## iter 60 value 15.754487
## iter 70 value 15.742685
## final value 15.742600
## converged
## # weights: 179
## initial value 113.028811
## iter 10 value 51.580245
## iter 20 value 18.290789
## iter 30 value 13.097499
## iter 40 value 12.212833
## iter 50 value 12.004191
## iter 60 value 11.909150
## iter 70 value 11.650546
## iter 80 value 11.634106
## iter 90 value 11.633821
## final value 11.633818
## converged
## # weights: 39
## initial value 115.982494
## iter 10 value 67.485232
## iter 20 value 52.358131
## iter 30 value 49.659574
## iter 40 value 47.370464
## iter 50 value 46.753621
## iter 60 value 46.544948
## iter 70 value 46.539404
## iter 80 value 46.497780
## iter 90 value 46.492026
## iter 100 value 45.739198
## final value 45.739198
## stopped after 100 iterations
## # weights: 109
## initial value 130.792341
## iter 10 value 55.816138
## iter 20 value 31.748520
## iter 30 value 29.668303
## iter 40 value 26.709486
## iter 50 value 24.513222
## iter 60 value 22.782674
## iter 70 value 18.797008
## iter 80 value 17.362251
## iter 90 value 16.918780
## iter 100 value 16.305872
## final value 16.305872
## stopped after 100 iterations
## # weights: 179
## initial value 104.611807
## iter 10 value 22.178543
## iter 20 value 12.448488
## iter 30 value 10.927914
## iter 40 value 9.463426
## iter 50 value 7.778377
## iter 60 value 7.736406
## iter 70 value 7.716093
## iter 80 value 7.708814
## iter 90 value 7.688677
## iter 100 value 6.568364
## final value 6.568364
## stopped after 100 iterations
## # weights: 39
## initial value 106.172496
## iter 10 value 86.872725
## iter 20 value 57.511076
## iter 30 value 56.540269
## iter 40 value 56.439194
## iter 50 value 56.396876
## iter 60 value 56.391441
## iter 70 value 56.391033
## iter 80 value 56.385723
## final value 56.385444
## converged
## # weights: 109
## initial value 104.772786
## iter 10 value 55.672629
## iter 20 value 25.962566
## iter 30 value 21.773805
## iter 40 value 18.892384
## iter 50 value 18.247111
## iter 60 value 17.457173
## iter 70 value 16.139747
## iter 80 value 15.793450
## iter 90 value 15.553146
## iter 100 value 15.233484
## final value 15.233484
## stopped after 100 iterations
## # weights: 179
## initial value 111.814271
## iter 10 value 44.354336
## iter 20 value 10.307670
## iter 30 value 6.086850
## iter 40 value 5.348094
## iter 50 value 3.498877
## iter 60 value 2.820113
## iter 70 value 2.793611
## iter 80 value 2.780488
## iter 90 value 2.764216
## iter 100 value 1.571160
## final value 1.571160
## stopped after 100 iterations
## # weights: 39
## initial value 104.974226
## iter 10 value 80.474975
## iter 20 value 71.828073
## iter 30 value 63.278804
## iter 40 value 61.888456
## iter 50 value 59.784916
## iter 60 value 59.612207
## iter 70 value 59.417355
## iter 80 value 58.905908
## iter 90 value 58.679049
## iter 100 value 58.372545
## final value 58.372545
## stopped after 100 iterations
## # weights: 109
## initial value 106.528293
## iter 10 value 53.734265
## iter 20 value 30.959814
## iter 30 value 23.095271
## iter 40 value 18.686541
## iter 50 value 17.210192
## iter 60 value 16.941136
## iter 70 value 16.866421
## iter 80 value 16.860315
## iter 90 value 16.860243
## iter 100 value 16.860240
## final value 16.860240
## stopped after 100 iterations
## # weights: 179
## initial value 106.609535
## iter 10 value 63.899599
## iter 20 value 25.022424
## iter 30 value 15.515251
## iter 40 value 13.810678
## iter 50 value 12.793296
## iter 60 value 12.484983
## iter 70 value 12.334758
## iter 80 value 12.236790
## iter 90 value 12.231608
## iter 100 value 12.231195
## final value 12.231195
## stopped after 100 iterations
## # weights: 39
## initial value 101.251697
## iter 10 value 63.047555
## iter 20 value 57.734503
## iter 30 value 56.939382
## iter 40 value 55.428482
## iter 50 value 54.867257
## iter 60 value 54.258537
## iter 70 value 54.032692
## iter 80 value 53.996665
## iter 90 value 53.631573
## iter 100 value 53.219971
## final value 53.219971
## stopped after 100 iterations
## # weights: 109
## initial value 108.635563
## iter 10 value 48.039839
## iter 20 value 37.102114
## iter 30 value 29.573146
## iter 40 value 26.880421
## iter 50 value 23.348759
## iter 60 value 18.988432
## iter 70 value 16.018643
## iter 80 value 13.120232
## iter 90 value 9.478768
## iter 100 value 7.288661
## final value 7.288661
## stopped after 100 iterations
## # weights: 179
## initial value 100.607660
## iter 10 value 27.004845
## iter 20 value 7.453921
## iter 30 value 5.302572
## iter 40 value 4.961924
## iter 50 value 4.927010
## iter 60 value 4.543250
## iter 70 value 4.290189
## iter 80 value 4.282470
## iter 90 value 4.276498
## iter 100 value 4.262330
## final value 4.262330
## stopped after 100 iterations
## # weights: 39
## initial value 100.717327
## iter 10 value 64.985780
## iter 20 value 58.567277
## iter 30 value 55.655203
## iter 40 value 53.682472
## iter 50 value 51.761930
## iter 60 value 50.823440
## iter 70 value 49.179080
## iter 80 value 48.890285
## iter 90 value 48.568348
## iter 100 value 48.209226
## final value 48.209226
## stopped after 100 iterations
## # weights: 109
## initial value 113.829643
## iter 10 value 50.596999
## iter 20 value 23.609407
## iter 30 value 18.619909
## iter 40 value 17.125002
## iter 50 value 15.399086
## iter 60 value 14.397348
## iter 70 value 5.728594
## iter 80 value 4.104008
## iter 90 value 3.936000
## iter 100 value 3.876241
## final value 3.876241
## stopped after 100 iterations
## # weights: 179
## initial value 104.998431
## iter 10 value 22.333531
## iter 20 value 13.811587
## iter 30 value 9.801557
## iter 40 value 8.153056
## iter 50 value 7.130349
## iter 60 value 4.183457
## iter 70 value 2.460700
## iter 80 value 1.938867
## iter 90 value 1.920188
## iter 100 value 1.915055
## final value 1.915055
## stopped after 100 iterations
## # weights: 39
## initial value 105.049947
## iter 10 value 81.858467
## iter 20 value 73.393758
## iter 30 value 65.001082
## iter 40 value 60.848524
## iter 50 value 59.022504
## iter 60 value 58.300377
## iter 70 value 57.178534
## iter 80 value 56.598609
## iter 90 value 56.595445
## iter 90 value 56.595444
## iter 90 value 56.595444
## final value 56.595444
## converged
## # weights: 109
## initial value 107.200565
## iter 10 value 67.677230
## iter 20 value 36.521978
## iter 30 value 22.392395
## iter 40 value 17.668750
## iter 50 value 17.246634
## iter 60 value 17.121434
## iter 70 value 17.061321
## iter 80 value 17.009409
## iter 90 value 16.442380
## iter 100 value 16.308005
## final value 16.308005
## stopped after 100 iterations
## # weights: 179
## initial value 152.069661
## iter 10 value 61.784433
## iter 20 value 27.333358
## iter 30 value 17.521918
## iter 40 value 14.049912
## iter 50 value 12.613819
## iter 60 value 12.224530
## iter 70 value 12.202828
## iter 80 value 12.202575
## final value 12.202574
## converged
## # weights: 39
## initial value 100.626011
## iter 10 value 65.509069
## iter 20 value 58.342048
## iter 30 value 54.963527
## iter 40 value 54.552171
## iter 50 value 54.531254
## iter 60 value 54.501058
## iter 70 value 54.491701
## iter 80 value 54.471611
## iter 90 value 54.470959
## iter 100 value 54.464181
## final value 54.464181
## stopped after 100 iterations
## # weights: 109
## initial value 116.438720
## iter 10 value 26.557513
## iter 20 value 15.497749
## iter 30 value 10.841419
## iter 40 value 8.225107
## iter 50 value 7.381404
## iter 60 value 7.358429
## iter 70 value 7.346883
## iter 80 value 7.343283
## iter 90 value 7.337369
## iter 100 value 7.324477
## final value 7.324477
## stopped after 100 iterations
## # weights: 179
## initial value 110.390245
## iter 10 value 21.362538
## iter 20 value 4.026865
## iter 30 value 1.539409
## iter 40 value 0.173331
## iter 50 value 0.153800
## iter 60 value 0.136164
## iter 70 value 0.127874
## iter 80 value 0.120223
## iter 90 value 0.112022
## iter 100 value 0.106387
## final value 0.106387
## stopped after 100 iterations
## # weights: 39
## initial value 98.552367
## iter 10 value 67.855438
## iter 20 value 63.206718
## iter 30 value 62.068004
## iter 40 value 60.066793
## iter 50 value 58.205316
## iter 60 value 56.389511
## iter 70 value 54.260845
## iter 80 value 54.025681
## iter 90 value 53.933156
## iter 100 value 53.914262
## final value 53.914262
## stopped after 100 iterations
## # weights: 109
## initial value 107.177071
## iter 10 value 51.032188
## iter 20 value 29.849584
## iter 30 value 17.488961
## iter 40 value 16.282317
## iter 50 value 14.886328
## iter 60 value 14.796254
## iter 70 value 14.794958
## iter 80 value 14.783471
## iter 90 value 14.764270
## iter 100 value 14.748210
## final value 14.748210
## stopped after 100 iterations
## # weights: 179
## initial value 110.349521
## iter 10 value 24.671307
## iter 20 value 9.924925
## iter 30 value 5.793526
## iter 40 value 3.025712
## iter 50 value 2.306541
## iter 60 value 1.683548
## iter 70 value 1.373242
## iter 80 value 0.016938
## iter 90 value 0.003044
## iter 100 value 0.001418
## final value 0.001418
## stopped after 100 iterations
## # weights: 39
## initial value 117.025048
## iter 10 value 69.939157
## iter 20 value 62.618639
## iter 30 value 60.193304
## iter 40 value 56.433804
## iter 50 value 55.966883
## iter 60 value 55.787166
## iter 70 value 55.643447
## final value 55.641012
## converged
## # weights: 109
## initial value 107.239992
## iter 10 value 60.210263
## iter 20 value 33.247721
## iter 30 value 20.005296
## iter 40 value 16.829320
## iter 50 value 16.611187
## iter 60 value 16.585789
## iter 70 value 16.576311
## iter 80 value 16.575989
## iter 90 value 16.575887
## iter 100 value 16.570244
## final value 16.570244
## stopped after 100 iterations
## # weights: 179
## initial value 98.070342
## iter 10 value 40.086104
## iter 20 value 20.348670
## iter 30 value 14.270639
## iter 40 value 12.530497
## iter 50 value 12.316868
## iter 60 value 12.177518
## iter 70 value 12.115303
## iter 80 value 12.104557
## final value 12.104504
## converged
## # weights: 39
## initial value 100.912213
## iter 10 value 70.384471
## iter 20 value 52.876164
## iter 30 value 46.449586
## iter 40 value 43.333689
## iter 50 value 41.598764
## iter 60 value 40.001865
## iter 70 value 38.421120
## iter 80 value 37.921205
## iter 90 value 36.137344
## iter 100 value 34.847697
## final value 34.847697
## stopped after 100 iterations
## # weights: 109
## initial value 112.716553
## iter 10 value 52.125195
## iter 20 value 39.553309
## iter 30 value 37.208279
## iter 40 value 36.981957
## iter 50 value 36.923396
## iter 60 value 32.555826
## iter 70 value 32.029213
## iter 80 value 30.925918
## iter 90 value 30.267182
## iter 100 value 29.472615
## final value 29.472615
## stopped after 100 iterations
## # weights: 179
## initial value 118.677646
## iter 10 value 28.638221
## iter 20 value 15.491842
## iter 30 value 13.136416
## iter 40 value 12.318244
## iter 50 value 12.228116
## iter 60 value 12.050837
## iter 70 value 11.857411
## iter 80 value 11.816401
## iter 90 value 11.801959
## iter 100 value 11.759083
## final value 11.759083
## stopped after 100 iterations
## # weights: 39
## initial value 99.880365
## iter 10 value 57.961515
## iter 20 value 56.762476
## iter 30 value 56.721989
## final value 56.721929
## converged
## # weights: 109
## initial value 96.754578
## iter 10 value 34.799439
## iter 20 value 21.553847
## iter 30 value 18.274166
## iter 40 value 16.807930
## iter 50 value 16.266340
## iter 60 value 15.922762
## iter 70 value 15.843743
## iter 80 value 15.794671
## iter 90 value 15.120971
## iter 100 value 14.882752
## final value 14.882752
## stopped after 100 iterations
## # weights: 179
## initial value 103.676180
## iter 10 value 33.171005
## iter 20 value 13.314321
## iter 30 value 7.562081
## iter 40 value 5.631887
## iter 50 value 5.125401
## iter 60 value 5.057361
## iter 70 value 5.033289
## iter 80 value 5.024343
## iter 90 value 4.188562
## iter 100 value 4.164907
## final value 4.164907
## stopped after 100 iterations
## # weights: 39
## initial value 109.231542
## iter 10 value 81.919137
## iter 20 value 71.954902
## iter 30 value 61.252902
## iter 40 value 59.000512
## iter 50 value 58.645675
## iter 60 value 58.209525
## iter 70 value 57.945934
## iter 80 value 57.894568
## iter 90 value 57.824541
## iter 100 value 57.793448
## final value 57.793448
## stopped after 100 iterations
## # weights: 109
## initial value 107.791837
## iter 10 value 53.681348
## iter 20 value 36.243093
## iter 30 value 28.404314
## iter 40 value 22.999579
## iter 50 value 19.119749
## iter 60 value 17.219522
## iter 70 value 16.420321
## iter 80 value 16.287253
## iter 90 value 16.258708
## iter 100 value 16.248457
## final value 16.248457
## stopped after 100 iterations
## # weights: 179
## initial value 115.324174
## iter 10 value 40.915611
## iter 20 value 24.398844
## iter 30 value 16.096842
## iter 40 value 13.897308
## iter 50 value 13.622497
## iter 60 value 12.734113
## iter 70 value 12.536133
## iter 80 value 12.485224
## iter 90 value 12.460506
## iter 100 value 12.459113
## final value 12.459113
## stopped after 100 iterations
## # weights: 39
## initial value 101.757311
## iter 10 value 68.407113
## iter 20 value 63.642736
## iter 30 value 62.402660
## iter 40 value 55.957965
## iter 50 value 53.515893
## iter 60 value 51.354498
## iter 70 value 50.607364
## iter 80 value 49.028315
## iter 90 value 48.682490
## iter 100 value 47.601503
## final value 47.601503
## stopped after 100 iterations
## # weights: 109
## initial value 103.194169
## iter 10 value 52.745907
## iter 20 value 41.613003
## iter 30 value 39.727312
## iter 40 value 37.670857
## iter 50 value 32.824255
## iter 60 value 29.672659
## iter 70 value 28.608905
## iter 80 value 27.764998
## iter 90 value 27.713414
## iter 100 value 27.028474
## final value 27.028474
## stopped after 100 iterations
## # weights: 179
## initial value 110.863530
## iter 10 value 27.436186
## iter 20 value 14.791227
## iter 30 value 10.701541
## iter 40 value 9.301095
## iter 50 value 8.630477
## iter 60 value 7.591234
## iter 70 value 4.815353
## iter 80 value 4.761468
## iter 90 value 4.754548
## iter 100 value 4.730066
## final value 4.730066
## stopped after 100 iterations
## # weights: 39
## initial value 110.959131
## iter 10 value 63.465868
## iter 20 value 58.488543
## iter 30 value 57.203294
## iter 40 value 57.110512
## iter 50 value 57.007346
## iter 60 value 56.496470
## iter 70 value 56.462252
## iter 80 value 56.443633
## iter 90 value 56.390091
## iter 100 value 52.483491
## final value 52.483491
## stopped after 100 iterations
## # weights: 109
## initial value 108.824751
## iter 10 value 47.460205
## iter 20 value 26.905718
## iter 30 value 20.341997
## iter 40 value 16.560115
## iter 50 value 15.997723
## iter 60 value 15.915869
## iter 70 value 15.900988
## iter 80 value 15.893432
## iter 90 value 15.884524
## iter 100 value 15.883344
## final value 15.883344
## stopped after 100 iterations
## # weights: 179
## initial value 111.206377
## iter 10 value 25.127204
## iter 20 value 7.266960
## iter 30 value 4.714106
## iter 40 value 1.481820
## iter 50 value 1.406999
## iter 60 value 1.393125
## iter 70 value 1.389522
## iter 80 value 1.387648
## iter 90 value 1.386778
## iter 100 value 1.386588
## final value 1.386588
## stopped after 100 iterations
## # weights: 39
## initial value 100.917487
## iter 10 value 71.981814
## iter 20 value 60.175294
## iter 30 value 58.317043
## iter 40 value 57.186037
## iter 50 value 56.272042
## iter 60 value 55.872294
## iter 70 value 55.864496
## iter 80 value 55.862892
## iter 80 value 55.862892
## final value 55.862892
## converged
## # weights: 109
## initial value 104.944030
## iter 10 value 50.983381
## iter 20 value 31.509779
## iter 30 value 24.463671
## iter 40 value 18.705393
## iter 50 value 17.301566
## iter 60 value 16.273576
## iter 70 value 16.074725
## iter 80 value 15.446333
## iter 90 value 15.324312
## iter 100 value 15.322977
## final value 15.322977
## stopped after 100 iterations
## # weights: 179
## initial value 119.735846
## iter 10 value 58.348497
## iter 20 value 26.209849
## iter 30 value 15.392135
## iter 40 value 13.540317
## iter 50 value 13.085016
## iter 60 value 12.674765
## iter 70 value 12.249233
## iter 80 value 12.138040
## iter 90 value 12.062072
## iter 100 value 12.012855
## final value 12.012855
## stopped after 100 iterations
## # weights: 39
## initial value 106.136459
## iter 10 value 65.889177
## iter 20 value 57.929137
## iter 30 value 56.948422
## iter 40 value 56.669705
## iter 50 value 56.637367
## iter 60 value 56.634346
## iter 70 value 55.888021
## iter 80 value 54.384829
## iter 90 value 51.968506
## iter 100 value 51.953527
## final value 51.953527
## stopped after 100 iterations
## # weights: 109
## initial value 102.682068
## iter 10 value 27.112006
## iter 20 value 9.241030
## iter 30 value 5.248934
## iter 40 value 3.790089
## iter 50 value 3.768558
## iter 60 value 3.743937
## iter 70 value 3.664266
## iter 80 value 3.643337
## iter 90 value 3.630319
## iter 100 value 3.623522
## final value 3.623522
## stopped after 100 iterations
## # weights: 179
## initial value 132.329856
## iter 10 value 40.464593
## iter 20 value 7.703621
## iter 30 value 0.637454
## iter 40 value 0.282670
## iter 50 value 0.249801
## iter 60 value 0.235429
## iter 70 value 0.213995
## iter 80 value 0.199750
## iter 90 value 0.186244
## iter 100 value 0.170999
## final value 0.170999
## stopped after 100 iterations
## # weights: 39
## initial value 107.932002
## iter 10 value 82.114107
## iter 20 value 71.940654
## iter 30 value 64.243677
## iter 40 value 62.468894
## iter 50 value 61.935604
## iter 60 value 60.912114
## iter 70 value 59.415782
## iter 80 value 58.338321
## iter 90 value 58.285187
## iter 100 value 57.979243
## final value 57.979243
## stopped after 100 iterations
## # weights: 109
## initial value 98.842008
## iter 10 value 28.955405
## iter 20 value 19.944667
## iter 30 value 17.611146
## iter 40 value 10.582223
## iter 50 value 7.940914
## iter 60 value 6.535773
## iter 70 value 6.225027
## iter 80 value 4.855722
## iter 90 value 4.820651
## iter 100 value 4.794305
## final value 4.794305
## stopped after 100 iterations
## # weights: 179
## initial value 99.691223
## iter 10 value 16.235017
## iter 20 value 6.053412
## iter 30 value 0.050231
## iter 40 value 0.001047
## final value 0.000067
## converged
## # weights: 39
## initial value 100.342119
## iter 10 value 66.064036
## iter 20 value 63.202448
## iter 30 value 61.353635
## iter 40 value 58.557329
## iter 50 value 58.329317
## iter 60 value 58.185655
## iter 70 value 57.935024
## iter 80 value 57.929060
## final value 57.929028
## converged
## # weights: 109
## initial value 114.883864
## iter 10 value 60.194812
## iter 20 value 31.552175
## iter 30 value 21.947784
## iter 40 value 17.649295
## iter 50 value 17.059606
## iter 60 value 17.042136
## iter 70 value 17.041686
## iter 70 value 17.041685
## iter 70 value 17.041685
## final value 17.041685
## converged
## # weights: 179
## initial value 135.344635
## iter 10 value 62.292829
## iter 20 value 35.416389
## iter 30 value 25.528215
## iter 40 value 17.108175
## iter 50 value 15.490671
## iter 60 value 13.508728
## iter 70 value 12.997724
## iter 80 value 12.481864
## iter 90 value 12.450390
## iter 100 value 12.449450
## final value 12.449450
## stopped after 100 iterations
## # weights: 39
## initial value 96.281811
## iter 10 value 63.609748
## iter 20 value 57.973872
## iter 30 value 55.453585
## iter 40 value 55.411477
## iter 50 value 55.345670
## iter 60 value 55.256349
## iter 70 value 55.206646
## iter 80 value 53.726004
## iter 90 value 53.687078
## iter 100 value 53.647198
## final value 53.647198
## stopped after 100 iterations
## # weights: 109
## initial value 103.866339
## iter 10 value 38.025011
## iter 20 value 27.001563
## iter 30 value 21.842538
## iter 40 value 19.261342
## iter 50 value 19.003653
## iter 60 value 18.894929
## iter 70 value 18.811323
## iter 80 value 18.660865
## iter 90 value 18.462815
## iter 100 value 18.333249
## final value 18.333249
## stopped after 100 iterations
## # weights: 179
## initial value 115.247467
## iter 10 value 19.718212
## iter 20 value 2.403281
## iter 30 value 0.186954
## iter 40 value 0.161772
## iter 50 value 0.149354
## iter 60 value 0.134385
## iter 70 value 0.126576
## iter 80 value 0.115716
## iter 90 value 0.103181
## iter 100 value 0.096828
## final value 0.096828
## stopped after 100 iterations
## # weights: 39
## initial value 108.894872
## iter 10 value 73.916422
## iter 20 value 65.266602
## iter 30 value 62.575629
## iter 40 value 61.718241
## iter 50 value 61.590989
## iter 60 value 61.578241
## iter 70 value 61.574426
## iter 80 value 61.572429
## iter 90 value 61.571914
## iter 100 value 61.571797
## final value 61.571797
## stopped after 100 iterations
## # weights: 109
## initial value 102.560674
## iter 10 value 30.690458
## iter 20 value 14.741499
## iter 30 value 13.482866
## iter 40 value 12.212442
## iter 50 value 12.124850
## iter 60 value 10.842659
## iter 70 value 10.657895
## iter 80 value 10.495840
## iter 90 value 10.482271
## iter 100 value 10.479345
## final value 10.479345
## stopped after 100 iterations
## # weights: 179
## initial value 105.150326
## iter 10 value 20.393961
## iter 20 value 5.560496
## iter 30 value 3.471349
## iter 40 value 3.297772
## iter 50 value 3.295724
## iter 60 value 2.340295
## iter 70 value 1.909851
## final value 1.909619
## converged
## # weights: 39
## initial value 110.168234
## iter 10 value 73.003745
## iter 20 value 64.755496
## iter 30 value 64.610060
## iter 40 value 64.605490
## final value 64.605478
## converged
## # weights: 109
## initial value 113.847105
## iter 10 value 44.279322
## iter 20 value 27.604761
## iter 30 value 21.034629
## iter 40 value 18.344250
## iter 50 value 16.931710
## iter 60 value 16.760816
## iter 70 value 16.754013
## iter 80 value 16.753892
## final value 16.753892
## converged
## # weights: 179
## initial value 113.759595
## iter 10 value 43.098184
## iter 20 value 20.631854
## iter 30 value 15.325356
## iter 40 value 13.401510
## iter 50 value 12.720648
## iter 60 value 12.631544
## iter 70 value 12.608085
## iter 80 value 12.558423
## iter 90 value 12.493229
## iter 100 value 12.490125
## final value 12.490125
## stopped after 100 iterations
## # weights: 39
## initial value 113.229508
## iter 10 value 72.061956
## iter 20 value 57.918778
## iter 30 value 55.499836
## iter 40 value 52.932819
## iter 50 value 52.587012
## iter 60 value 52.578704
## iter 70 value 52.534078
## iter 80 value 51.803951
## iter 90 value 51.656701
## iter 100 value 51.644961
## final value 51.644961
## stopped after 100 iterations
## # weights: 109
## initial value 107.200968
## iter 10 value 46.784343
## iter 20 value 22.831097
## iter 30 value 8.638815
## iter 40 value 3.962849
## iter 50 value 2.730250
## iter 60 value 2.307990
## iter 70 value 2.256712
## iter 80 value 2.239470
## iter 90 value 2.232780
## iter 100 value 2.220600
## final value 2.220600
## stopped after 100 iterations
## # weights: 179
## initial value 147.812819
## iter 10 value 50.119464
## iter 20 value 15.137491
## iter 30 value 4.521312
## iter 40 value 0.406948
## iter 50 value 0.160106
## iter 60 value 0.143188
## iter 70 value 0.136240
## iter 80 value 0.127416
## iter 90 value 0.119076
## iter 100 value 0.106773
## final value 0.106773
## stopped after 100 iterations
## # weights: 39
## initial value 96.754508
## iter 10 value 63.341723
## iter 20 value 52.047592
## iter 30 value 47.012724
## iter 40 value 44.223938
## iter 50 value 42.665768
## iter 60 value 42.446280
## iter 70 value 42.385498
## iter 80 value 42.312469
## iter 90 value 42.296377
## iter 100 value 42.293163
## final value 42.293163
## stopped after 100 iterations
## # weights: 109
## initial value 107.598829
## iter 10 value 40.357649
## iter 20 value 4.992283
## iter 30 value 1.932641
## iter 40 value 1.388108
## iter 50 value 1.386534
## iter 60 value 1.386490
## iter 70 value 1.386341
## final value 1.386341
## converged
## # weights: 179
## initial value 130.835202
## iter 10 value 47.799740
## iter 20 value 28.302585
## iter 30 value 25.568887
## iter 40 value 24.101009
## iter 50 value 23.653896
## iter 60 value 23.487635
## iter 70 value 19.512113
## iter 80 value 14.864137
## iter 90 value 13.195525
## iter 100 value 8.790588
## final value 8.790588
## stopped after 100 iterations
## # weights: 39
## initial value 117.625333
## iter 10 value 84.737886
## iter 20 value 76.168626
## iter 30 value 70.701832
## iter 40 value 67.513768
## iter 50 value 66.968536
## iter 60 value 66.961837
## iter 70 value 66.961178
## final value 66.961156
## converged
## # weights: 109
## initial value 114.068774
## iter 10 value 44.952273
## iter 20 value 25.753066
## iter 30 value 19.603756
## iter 40 value 16.553390
## iter 50 value 16.409820
## iter 60 value 16.403952
## final value 16.403938
## converged
## # weights: 179
## initial value 104.955856
## iter 10 value 55.690905
## iter 20 value 24.103895
## iter 30 value 16.377522
## iter 40 value 13.678623
## iter 50 value 12.943146
## iter 60 value 12.451581
## iter 70 value 12.159373
## iter 80 value 12.017555
## iter 90 value 11.987827
## iter 100 value 11.985743
## final value 11.985743
## stopped after 100 iterations
## # weights: 39
## initial value 117.782625
## iter 10 value 64.787896
## iter 20 value 56.506052
## iter 30 value 51.372711
## iter 40 value 49.728805
## iter 50 value 46.880625
## iter 60 value 46.290194
## iter 70 value 45.209927
## iter 80 value 45.199754
## iter 90 value 40.538038
## iter 100 value 38.783141
## final value 38.783141
## stopped after 100 iterations
## # weights: 109
## initial value 105.848269
## iter 10 value 18.267208
## iter 20 value 3.891816
## iter 30 value 3.804844
## iter 40 value 3.630826
## iter 50 value 3.567383
## iter 60 value 3.531178
## iter 70 value 2.338964
## iter 80 value 1.511577
## iter 90 value 1.482782
## iter 100 value 0.213127
## final value 0.213127
## stopped after 100 iterations
## # weights: 179
## initial value 119.754249
## iter 10 value 19.431984
## iter 20 value 8.523413
## iter 30 value 4.887144
## iter 40 value 3.219587
## iter 50 value 2.276421
## iter 60 value 2.099917
## iter 70 value 2.055116
## iter 80 value 2.039856
## iter 90 value 2.032664
## iter 100 value 2.022019
## final value 2.022019
## stopped after 100 iterations
## # weights: 39
## initial value 104.764480
## iter 10 value 63.246282
## iter 20 value 61.323781
## iter 30 value 60.611185
## iter 40 value 60.519722
## iter 50 value 57.711113
## iter 60 value 54.692006
## iter 70 value 52.427619
## iter 80 value 51.925607
## iter 90 value 51.238292
## iter 100 value 50.944807
## final value 50.944807
## stopped after 100 iterations
## # weights: 109
## initial value 103.804462
## iter 10 value 47.808466
## iter 20 value 27.109549
## iter 30 value 25.265803
## iter 40 value 23.525787
## iter 50 value 23.256232
## iter 60 value 21.116967
## iter 70 value 20.790721
## iter 80 value 20.503890
## iter 90 value 20.387481
## iter 100 value 17.741720
## final value 17.741720
## stopped after 100 iterations
## # weights: 179
## initial value 114.975822
## iter 10 value 23.694777
## iter 20 value 10.433985
## iter 30 value 1.263466
## iter 40 value 0.058917
## iter 50 value 0.008678
## iter 60 value 0.003870
## iter 70 value 0.001816
## iter 80 value 0.001265
## iter 90 value 0.000424
## iter 100 value 0.000275
## final value 0.000275
## stopped after 100 iterations
## # weights: 39
## initial value 120.569025
## iter 10 value 71.814694
## iter 20 value 63.688957
## iter 30 value 61.620733
## iter 40 value 58.511222
## iter 50 value 57.961639
## iter 60 value 57.444182
## iter 70 value 57.377931
## iter 80 value 57.003372
## iter 90 value 56.434286
## iter 100 value 56.196210
## final value 56.196210
## stopped after 100 iterations
## # weights: 109
## initial value 103.408545
## iter 10 value 47.612621
## iter 20 value 28.336631
## iter 30 value 22.684814
## iter 40 value 18.948529
## iter 50 value 17.592884
## iter 60 value 16.588068
## iter 70 value 16.519867
## iter 80 value 16.518014
## final value 16.518009
## converged
## # weights: 179
## initial value 104.308140
## iter 10 value 50.166380
## iter 20 value 23.371493
## iter 30 value 14.335170
## iter 40 value 13.686882
## iter 50 value 13.601760
## iter 60 value 13.250208
## iter 70 value 12.900405
## iter 80 value 12.867244
## iter 90 value 12.820714
## iter 100 value 12.756087
## final value 12.756087
## stopped after 100 iterations
## # weights: 39
## initial value 120.621421
## iter 10 value 74.875171
## iter 20 value 56.586052
## iter 30 value 51.122404
## iter 40 value 47.262118
## iter 50 value 45.469478
## iter 60 value 45.313649
## iter 70 value 44.885904
## iter 80 value 44.826018
## iter 90 value 44.731634
## iter 100 value 44.687273
## final value 44.687273
## stopped after 100 iterations
## # weights: 109
## initial value 113.421039
## iter 10 value 31.834062
## iter 20 value 17.352883
## iter 30 value 12.351665
## iter 40 value 12.045616
## iter 50 value 11.909739
## iter 60 value 11.883491
## iter 70 value 11.765309
## iter 80 value 11.647654
## iter 90 value 11.577306
## iter 100 value 11.532177
## final value 11.532177
## stopped after 100 iterations
## # weights: 179
## initial value 91.351331
## iter 10 value 17.184451
## iter 20 value 0.628226
## iter 30 value 0.246212
## iter 40 value 0.230224
## iter 50 value 0.199532
## iter 60 value 0.175362
## iter 70 value 0.159984
## iter 80 value 0.150411
## iter 90 value 0.143870
## iter 100 value 0.138255
## final value 0.138255
## stopped after 100 iterations
## # weights: 39
## initial value 110.449141
## iter 10 value 67.797707
## iter 20 value 61.818435
## iter 30 value 61.011241
## iter 40 value 60.216677
## iter 50 value 55.187009
## iter 60 value 54.910575
## iter 70 value 54.099737
## iter 80 value 54.092101
## iter 90 value 54.082945
## iter 100 value 54.082830
## final value 54.082830
## stopped after 100 iterations
## # weights: 109
## initial value 102.533147
## iter 10 value 55.427692
## iter 20 value 34.586424
## iter 30 value 25.402731
## iter 40 value 19.149263
## iter 50 value 15.670454
## iter 60 value 13.630141
## iter 70 value 13.410338
## iter 80 value 11.456949
## iter 90 value 11.296778
## iter 100 value 11.257873
## final value 11.257873
## stopped after 100 iterations
## # weights: 179
## initial value 130.377959
## iter 10 value 32.501684
## iter 20 value 2.826998
## iter 30 value 0.052304
## iter 40 value 0.000183
## final value 0.000081
## converged
## # weights: 39
## initial value 104.728586
## iter 10 value 79.749280
## iter 20 value 67.331347
## iter 30 value 59.104183
## iter 40 value 56.931520
## iter 50 value 56.511162
## iter 60 value 56.263544
## iter 70 value 56.082908
## iter 80 value 56.010198
## final value 56.009809
## converged
## # weights: 109
## initial value 122.370198
## iter 10 value 58.603496
## iter 20 value 33.589782
## iter 30 value 21.254683
## iter 40 value 17.517896
## iter 50 value 15.869222
## iter 60 value 15.408248
## iter 70 value 15.372602
## iter 80 value 15.368923
## iter 90 value 15.368778
## final value 15.368777
## converged
## # weights: 179
## initial value 104.607661
## iter 10 value 38.735094
## iter 20 value 17.333637
## iter 30 value 13.194963
## iter 40 value 12.507821
## iter 50 value 12.387834
## iter 60 value 12.361019
## iter 70 value 12.359437
## iter 80 value 12.359250
## final value 12.359247
## converged
## # weights: 39
## initial value 101.726800
## iter 10 value 66.283439
## iter 20 value 57.303158
## iter 30 value 56.336324
## iter 40 value 55.237768
## iter 50 value 54.294321
## iter 60 value 53.968806
## iter 70 value 51.064236
## iter 80 value 50.433134
## iter 90 value 49.098840
## iter 100 value 48.450998
## final value 48.450998
## stopped after 100 iterations
## # weights: 109
## initial value 111.955568
## iter 10 value 78.479271
## iter 20 value 43.163637
## iter 30 value 27.507234
## iter 40 value 21.873629
## iter 50 value 20.352318
## iter 60 value 13.565364
## iter 70 value 13.513562
## iter 80 value 13.499072
## iter 90 value 13.288446
## iter 100 value 10.412232
## final value 10.412232
## stopped after 100 iterations
## # weights: 179
## initial value 107.997271
## iter 10 value 22.674823
## iter 20 value 6.362081
## iter 30 value 2.763221
## iter 40 value 2.717509
## iter 50 value 2.696336
## iter 60 value 2.465613
## iter 70 value 2.081662
## iter 80 value 1.471484
## iter 90 value 0.172761
## iter 100 value 0.155574
## final value 0.155574
## stopped after 100 iterations
## # weights: 39
## initial value 103.886470
## iter 10 value 67.344386
## iter 20 value 64.551048
## iter 30 value 63.754207
## iter 40 value 63.460116
## iter 50 value 63.426192
## iter 60 value 63.421501
## iter 70 value 63.421203
## iter 80 value 63.420945
## iter 90 value 63.420830
## final value 63.420813
## converged
## # weights: 109
## initial value 107.331717
## iter 10 value 34.019434
## iter 20 value 24.313956
## iter 30 value 19.528021
## iter 40 value 18.793431
## iter 50 value 15.314331
## iter 60 value 14.700858
## iter 70 value 8.868329
## iter 80 value 8.353681
## iter 90 value 8.055213
## iter 100 value 7.921747
## final value 7.921747
## stopped after 100 iterations
## # weights: 179
## initial value 119.341946
## iter 10 value 29.260662
## iter 20 value 10.545634
## iter 30 value 5.544076
## iter 40 value 3.055880
## iter 50 value 2.647555
## iter 60 value 0.096519
## iter 70 value 0.038205
## iter 80 value 0.013386
## iter 90 value 0.005842
## iter 100 value 0.001180
## final value 0.001180
## stopped after 100 iterations
## # weights: 39
## initial value 107.943692
## iter 10 value 73.184575
## iter 20 value 65.641014
## iter 30 value 63.547644
## iter 40 value 60.300469
## iter 50 value 60.049537
## iter 60 value 59.579260
## iter 70 value 59.106109
## iter 80 value 58.611613
## iter 90 value 57.879263
## iter 100 value 57.624212
## final value 57.624212
## stopped after 100 iterations
## # weights: 109
## initial value 103.475084
## iter 10 value 53.919216
## iter 20 value 29.723484
## iter 30 value 21.664634
## iter 40 value 19.160742
## iter 50 value 18.748583
## iter 60 value 18.602662
## iter 70 value 17.131767
## iter 80 value 16.901708
## iter 90 value 16.894407
## iter 100 value 16.892603
## final value 16.892603
## stopped after 100 iterations
## # weights: 179
## initial value 108.097968
## iter 10 value 48.924665
## iter 20 value 24.858059
## iter 30 value 15.815989
## iter 40 value 13.403120
## iter 50 value 12.614113
## iter 60 value 12.460090
## iter 70 value 12.450702
## iter 80 value 12.450540
## final value 12.450536
## converged
## # weights: 39
## initial value 115.454924
## iter 10 value 71.413989
## iter 20 value 62.974229
## iter 30 value 56.606915
## iter 40 value 53.905331
## iter 50 value 53.896478
## iter 60 value 53.892414
## iter 70 value 53.887586
## iter 80 value 52.767773
## iter 90 value 52.751005
## iter 100 value 52.748771
## final value 52.748771
## stopped after 100 iterations
## # weights: 109
## initial value 113.938518
## iter 10 value 35.060278
## iter 20 value 27.042466
## iter 30 value 13.422112
## iter 40 value 6.748456
## iter 50 value 5.786346
## iter 60 value 5.668109
## iter 70 value 5.637586
## iter 80 value 2.820368
## iter 90 value 2.225010
## iter 100 value 2.186598
## final value 2.186598
## stopped after 100 iterations
## # weights: 179
## initial value 118.218415
## iter 10 value 25.909847
## iter 20 value 12.803819
## iter 30 value 6.989589
## iter 40 value 6.510152
## iter 50 value 6.433262
## iter 60 value 6.398586
## iter 70 value 6.368578
## iter 80 value 5.308452
## iter 90 value 4.863248
## iter 100 value 2.780022
## final value 2.780022
## stopped after 100 iterations
## # weights: 39
## initial value 111.018778
## iter 10 value 62.708955
## iter 20 value 56.069550
## iter 30 value 54.005286
## iter 40 value 52.125735
## iter 50 value 51.234402
## iter 60 value 50.238236
## iter 70 value 49.806295
## iter 80 value 49.316557
## iter 90 value 48.005040
## iter 100 value 47.298507
## final value 47.298507
## stopped after 100 iterations
## # weights: 109
## initial value 117.907953
## iter 10 value 38.436704
## iter 20 value 24.825572
## iter 30 value 22.416181
## iter 40 value 20.491744
## iter 50 value 20.275936
## iter 60 value 20.235507
## iter 70 value 20.184980
## iter 80 value 20.139252
## iter 90 value 20.041602
## iter 100 value 19.864983
## final value 19.864983
## stopped after 100 iterations
## # weights: 179
## initial value 105.328276
## iter 10 value 20.402097
## iter 20 value 8.646863
## iter 30 value 7.759287
## iter 40 value 6.427525
## iter 50 value 6.329033
## iter 60 value 6.314727
## iter 70 value 5.948188
## iter 80 value 5.225066
## iter 90 value 1.464483
## iter 100 value 1.399467
## final value 1.399467
## stopped after 100 iterations
## # weights: 39
## initial value 107.950372
## iter 10 value 77.465959
## iter 20 value 72.231504
## iter 30 value 66.943362
## iter 40 value 59.435617
## iter 50 value 58.790556
## iter 60 value 58.583038
## iter 70 value 58.532780
## iter 80 value 58.528483
## final value 58.528371
## converged
## # weights: 109
## initial value 104.885899
## iter 10 value 44.535130
## iter 20 value 28.851385
## iter 30 value 20.433509
## iter 40 value 17.720537
## iter 50 value 17.073965
## iter 60 value 16.949024
## iter 70 value 16.627893
## iter 80 value 15.979658
## iter 90 value 15.968381
## final value 15.968214
## converged
## # weights: 179
## initial value 135.006360
## iter 10 value 40.221471
## iter 20 value 24.276930
## iter 30 value 15.254512
## iter 40 value 13.772375
## iter 50 value 12.933977
## iter 60 value 12.535304
## iter 70 value 12.415618
## iter 80 value 12.399639
## iter 90 value 12.398837
## final value 12.398817
## converged
## # weights: 39
## initial value 102.252185
## iter 10 value 57.893798
## iter 20 value 54.164271
## iter 30 value 52.992441
## iter 40 value 52.401022
## iter 50 value 51.459193
## iter 60 value 50.369940
## iter 70 value 49.350936
## iter 80 value 49.163109
## iter 90 value 48.416435
## iter 100 value 47.945262
## final value 47.945262
## stopped after 100 iterations
## # weights: 109
## initial value 115.785334
## iter 10 value 24.391870
## iter 20 value 12.769583
## iter 30 value 8.410923
## iter 40 value 8.189229
## iter 50 value 8.172680
## iter 60 value 8.155788
## iter 70 value 8.125105
## iter 80 value 8.078362
## iter 90 value 8.056941
## iter 100 value 8.050334
## final value 8.050334
## stopped after 100 iterations
## # weights: 179
## initial value 134.189972
## iter 10 value 52.438019
## iter 20 value 18.773631
## iter 30 value 12.851951
## iter 40 value 9.990585
## iter 50 value 9.809519
## iter 60 value 9.652385
## iter 70 value 9.629092
## iter 80 value 9.614704
## iter 90 value 9.597432
## iter 100 value 9.568336
## final value 9.568336
## stopped after 100 iterations
## # weights: 39
## initial value 104.866968
## iter 10 value 73.693383
## iter 20 value 68.395897
## iter 30 value 67.734504
## iter 40 value 67.081068
## iter 50 value 64.541467
## iter 60 value 62.515229
## iter 70 value 62.301896
## iter 80 value 61.067795
## iter 90 value 61.040956
## iter 100 value 61.013387
## final value 61.013387
## stopped after 100 iterations
## # weights: 109
## initial value 103.240359
## iter 10 value 39.397373
## iter 20 value 28.145510
## iter 30 value 22.790912
## iter 40 value 17.300873
## iter 50 value 13.603440
## iter 60 value 11.692283
## iter 70 value 8.900997
## iter 80 value 7.206432
## iter 90 value 5.569697
## iter 100 value 5.423758
## final value 5.423758
## stopped after 100 iterations
## # weights: 179
## initial value 107.657019
## iter 10 value 17.318848
## iter 20 value 4.847240
## iter 30 value 2.824152
## iter 40 value 1.899925
## iter 50 value 1.410387
## iter 60 value 0.014237
## iter 70 value 0.005619
## iter 80 value 0.002739
## iter 90 value 0.001787
## iter 100 value 0.001287
## final value 0.001287
## stopped after 100 iterations
## # weights: 39
## initial value 115.940116
## iter 10 value 80.433108
## iter 20 value 64.166528
## iter 30 value 60.732525
## iter 40 value 59.635453
## iter 50 value 59.078226
## iter 60 value 58.995451
## iter 70 value 58.780248
## iter 80 value 58.340269
## iter 90 value 58.153292
## iter 100 value 58.142893
## final value 58.142893
## stopped after 100 iterations
## # weights: 109
## initial value 109.906160
## iter 10 value 44.732187
## iter 20 value 32.476660
## iter 30 value 22.268931
## iter 40 value 18.126868
## iter 50 value 17.171169
## iter 60 value 17.071029
## iter 70 value 16.989255
## iter 80 value 16.714127
## iter 90 value 16.706393
## final value 16.706373
## converged
## # weights: 179
## initial value 121.964959
## iter 10 value 32.843419
## iter 20 value 18.458923
## iter 30 value 13.165546
## iter 40 value 12.555487
## iter 50 value 12.510685
## iter 60 value 12.493080
## iter 70 value 12.490919
## iter 80 value 12.490658
## iter 90 value 12.490643
## final value 12.490642
## converged
## # weights: 39
## initial value 100.338530
## iter 10 value 76.169395
## iter 20 value 70.448455
## iter 30 value 54.246536
## iter 40 value 48.802907
## iter 50 value 47.175589
## iter 60 value 47.107765
## iter 70 value 47.054691
## iter 80 value 47.026156
## iter 90 value 47.017621
## iter 100 value 47.010133
## final value 47.010133
## stopped after 100 iterations
## # weights: 109
## initial value 91.979990
## iter 10 value 30.707373
## iter 20 value 21.919875
## iter 30 value 17.714452
## iter 40 value 12.383581
## iter 50 value 9.600865
## iter 60 value 6.411896
## iter 70 value 6.114726
## iter 80 value 5.815612
## iter 90 value 5.528974
## iter 100 value 4.984915
## final value 4.984915
## stopped after 100 iterations
## # weights: 179
## initial value 90.186043
## iter 10 value 12.282116
## iter 20 value 1.873845
## iter 30 value 1.507811
## iter 40 value 0.473736
## iter 50 value 0.144523
## iter 60 value 0.123729
## iter 70 value 0.118167
## iter 80 value 0.112059
## iter 90 value 0.103234
## iter 100 value 0.095990
## final value 0.095990
## stopped after 100 iterations
## # weights: 39
## initial value 101.796799
## iter 10 value 72.835712
## iter 20 value 70.430026
## iter 30 value 70.013493
## iter 40 value 69.973718
## iter 50 value 69.968970
## iter 60 value 69.968507
## final value 69.967892
## converged
## # weights: 109
## initial value 114.626117
## iter 10 value 38.527097
## iter 20 value 24.571755
## iter 30 value 21.572643
## iter 40 value 20.168706
## iter 50 value 16.688982
## iter 60 value 12.992024
## iter 70 value 12.423947
## iter 80 value 11.798798
## iter 90 value 9.992020
## iter 100 value 7.020788
## final value 7.020788
## stopped after 100 iterations
## # weights: 179
## initial value 108.547980
## iter 10 value 27.010044
## iter 20 value 3.617901
## iter 30 value 1.928378
## iter 40 value 1.911100
## iter 50 value 1.909862
## iter 60 value 1.909543
## final value 1.909543
## converged
## # weights: 39
## initial value 100.239706
## iter 10 value 78.694682
## iter 20 value 67.427987
## iter 30 value 64.626249
## iter 40 value 61.717163
## iter 50 value 60.361951
## iter 60 value 60.288245
## final value 60.288213
## converged
## # weights: 109
## initial value 106.313650
## iter 10 value 43.068440
## iter 20 value 28.983081
## iter 30 value 22.984883
## iter 40 value 17.894523
## iter 50 value 17.235365
## iter 60 value 16.915588
## iter 70 value 16.358464
## iter 80 value 15.798334
## iter 90 value 15.794051
## final value 15.794037
## converged
## # weights: 179
## initial value 117.629130
## iter 10 value 41.854606
## iter 20 value 26.693568
## iter 30 value 18.463893
## iter 40 value 13.978631
## iter 50 value 12.741489
## iter 60 value 12.604487
## iter 70 value 12.585013
## iter 80 value 12.583861
## final value 12.583839
## converged
## # weights: 39
## initial value 103.631835
## iter 10 value 65.396030
## iter 20 value 54.768146
## iter 30 value 37.966065
## iter 40 value 35.718768
## iter 50 value 35.701729
## iter 60 value 35.688947
## iter 70 value 35.678597
## iter 80 value 35.666540
## iter 90 value 35.663116
## iter 100 value 35.660045
## final value 35.660045
## stopped after 100 iterations
## # weights: 109
## initial value 117.118302
## iter 10 value 35.340095
## iter 20 value 17.220076
## iter 30 value 14.223567
## iter 40 value 14.030370
## iter 50 value 13.894558
## iter 60 value 13.647203
## iter 70 value 13.204645
## iter 80 value 13.051009
## iter 90 value 12.962994
## iter 100 value 12.929135
## final value 12.929135
## stopped after 100 iterations
## # weights: 179
## initial value 101.943250
## iter 10 value 21.042763
## iter 20 value 5.771815
## iter 30 value 3.656828
## iter 40 value 3.471391
## iter 50 value 1.991634
## iter 60 value 1.603838
## iter 70 value 1.553675
## iter 80 value 1.546291
## iter 90 value 1.532752
## iter 100 value 1.520293
## final value 1.520293
## stopped after 100 iterations
## # weights: 39
## initial value 99.797558
## iter 10 value 65.945680
## iter 20 value 57.633580
## iter 30 value 54.165715
## iter 40 value 53.010454
## iter 50 value 51.946576
## iter 60 value 50.192814
## iter 70 value 48.590970
## iter 80 value 48.269517
## iter 90 value 48.131258
## iter 100 value 47.582145
## final value 47.582145
## stopped after 100 iterations
## # weights: 109
## initial value 97.850406
## iter 10 value 41.959992
## iter 20 value 10.897053
## iter 30 value 5.645714
## iter 40 value 5.109703
## iter 50 value 3.724841
## iter 60 value 3.404521
## iter 70 value 3.084039
## iter 80 value 2.927412
## iter 90 value 2.753889
## iter 100 value 2.547055
## final value 2.547055
## stopped after 100 iterations
## # weights: 179
## initial value 112.764363
## iter 10 value 23.686353
## iter 20 value 3.595541
## iter 30 value 2.243603
## iter 40 value 1.967209
## iter 50 value 1.914465
## iter 60 value 1.910244
## iter 70 value 1.512484
## iter 80 value 0.009200
## iter 90 value 0.003815
## iter 100 value 0.001384
## final value 0.001384
## stopped after 100 iterations
## # weights: 39
## initial value 115.324800
## iter 10 value 67.952091
## iter 20 value 63.175048
## iter 30 value 60.835177
## iter 40 value 58.722498
## iter 50 value 56.706761
## iter 60 value 56.624996
## iter 70 value 56.624357
## final value 56.624349
## converged
## # weights: 109
## initial value 108.072009
## iter 10 value 48.128830
## iter 20 value 30.797125
## iter 30 value 23.500813
## iter 40 value 17.361479
## iter 50 value 15.796159
## iter 60 value 15.355431
## iter 70 value 14.347634
## iter 80 value 14.254360
## final value 14.254081
## converged
## # weights: 179
## initial value 125.039959
## iter 10 value 30.179099
## iter 20 value 14.638748
## iter 30 value 12.198245
## iter 40 value 11.678432
## iter 50 value 11.606010
## iter 60 value 11.599622
## iter 70 value 11.599551
## final value 11.599551
## converged
## # weights: 39
## initial value 107.627648
## iter 10 value 65.001464
## iter 20 value 61.205110
## iter 30 value 57.776204
## iter 40 value 57.720982
## iter 50 value 57.706946
## iter 60 value 57.246883
## iter 70 value 56.640924
## iter 80 value 56.373159
## iter 90 value 55.755415
## iter 100 value 55.458089
## final value 55.458089
## stopped after 100 iterations
## # weights: 109
## initial value 108.253895
## iter 10 value 29.477915
## iter 20 value 22.458155
## iter 30 value 11.034093
## iter 40 value 10.664937
## iter 50 value 6.323761
## iter 60 value 5.354258
## iter 70 value 5.290192
## iter 80 value 5.274492
## iter 90 value 5.151863
## iter 100 value 4.912353
## final value 4.912353
## stopped after 100 iterations
## # weights: 179
## initial value 126.248823
## iter 10 value 17.031883
## iter 20 value 2.573441
## iter 30 value 2.423008
## iter 40 value 2.397704
## iter 50 value 2.382752
## iter 60 value 1.513084
## iter 70 value 0.350666
## iter 80 value 0.181823
## iter 90 value 0.172457
## iter 100 value 0.165114
## final value 0.165114
## stopped after 100 iterations
## # weights: 39
## initial value 99.092512
## iter 10 value 54.520354
## iter 20 value 52.461179
## iter 30 value 51.131887
## iter 40 value 51.123527
## iter 50 value 51.122706
## final value 51.122704
## converged
## # weights: 109
## initial value 104.389699
## iter 10 value 27.861184
## iter 20 value 16.934191
## iter 30 value 13.458953
## iter 40 value 11.705939
## iter 50 value 11.395261
## iter 60 value 10.838357
## iter 70 value 10.635628
## iter 80 value 10.235096
## iter 90 value 10.177024
## iter 100 value 10.115460
## final value 10.115460
## stopped after 100 iterations
## # weights: 179
## initial value 101.802878
## iter 10 value 25.946736
## iter 20 value 5.998618
## iter 30 value 2.284677
## iter 40 value 0.292031
## iter 50 value 0.024886
## iter 60 value 0.002933
## iter 70 value 0.000841
## iter 80 value 0.000362
## iter 90 value 0.000125
## iter 90 value 0.000078
## iter 90 value 0.000078
## final value 0.000078
## converged
## # weights: 39
## initial value 106.491510
## iter 10 value 81.936443
## iter 20 value 66.829675
## iter 30 value 58.211194
## iter 40 value 57.699985
## iter 50 value 57.166752
## iter 60 value 57.081910
## final value 57.081874
## converged
## # weights: 109
## initial value 112.703198
## iter 10 value 50.955553
## iter 20 value 34.678609
## iter 30 value 21.833766
## iter 40 value 16.577879
## iter 50 value 15.680038
## iter 60 value 15.480145
## iter 70 value 15.270827
## iter 80 value 15.266449
## final value 15.266422
## converged
## # weights: 179
## initial value 110.261485
## iter 10 value 40.124388
## iter 20 value 20.354962
## iter 30 value 13.681031
## iter 40 value 12.657736
## iter 50 value 12.231811
## iter 60 value 12.161312
## iter 70 value 12.081673
## iter 80 value 12.018442
## iter 90 value 12.017153
## final value 12.017153
## converged
## # weights: 39
## initial value 115.541619
## iter 10 value 69.763455
## iter 20 value 56.761979
## iter 30 value 56.176660
## iter 40 value 55.957441
## iter 50 value 55.498158
## iter 60 value 54.784496
## iter 70 value 54.614732
## iter 80 value 53.985752
## iter 90 value 53.746176
## iter 100 value 53.734136
## final value 53.734136
## stopped after 100 iterations
## # weights: 109
## initial value 101.441106
## iter 10 value 50.608195
## iter 20 value 29.747735
## iter 30 value 23.999537
## iter 40 value 22.719029
## iter 50 value 22.692488
## iter 60 value 22.288452
## iter 70 value 20.883098
## iter 80 value 20.707498
## iter 90 value 20.662697
## iter 100 value 20.628856
## final value 20.628856
## stopped after 100 iterations
## # weights: 179
## initial value 99.230238
## iter 10 value 26.101055
## iter 20 value 4.938801
## iter 30 value 1.932110
## iter 40 value 0.654284
## iter 50 value 0.330809
## iter 60 value 0.316018
## iter 70 value 0.297297
## iter 80 value 0.287929
## iter 90 value 0.271203
## iter 100 value 0.256847
## final value 0.256847
## stopped after 100 iterations
## # weights: 39
## initial value 104.669963
## iter 10 value 66.864078
## iter 20 value 57.140708
## iter 30 value 46.290808
## iter 40 value 42.824948
## iter 50 value 39.941510
## iter 60 value 39.464018
## iter 70 value 39.274388
## iter 80 value 39.192357
## iter 90 value 39.187777
## final value 39.187680
## converged
## # weights: 109
## initial value 102.806021
## iter 10 value 34.130442
## iter 20 value 18.861193
## iter 30 value 15.530283
## iter 40 value 14.860458
## iter 50 value 13.471908
## iter 60 value 12.538710
## iter 70 value 12.487662
## iter 80 value 12.462002
## iter 90 value 10.310143
## iter 100 value 10.248749
## final value 10.248749
## stopped after 100 iterations
## # weights: 179
## initial value 110.056506
## iter 10 value 29.548531
## iter 20 value 7.109959
## iter 30 value 0.138636
## iter 40 value 0.004803
## iter 50 value 0.000520
## final value 0.000079
## converged
## # weights: 39
## initial value 107.606755
## iter 10 value 64.890558
## iter 20 value 62.218865
## iter 30 value 60.288694
## iter 40 value 59.565045
## iter 50 value 59.470008
## iter 60 value 59.438217
## iter 70 value 59.288267
## iter 80 value 58.854671
## iter 90 value 58.320744
## iter 100 value 57.969901
## final value 57.969901
## stopped after 100 iterations
## # weights: 109
## initial value 103.328452
## iter 10 value 45.950875
## iter 20 value 27.123260
## iter 30 value 21.293860
## iter 40 value 18.066032
## iter 50 value 16.870077
## iter 60 value 16.818723
## iter 70 value 16.818030
## final value 16.818018
## converged
## # weights: 179
## initial value 113.379353
## iter 10 value 35.821643
## iter 20 value 19.753527
## iter 30 value 15.336133
## iter 40 value 13.298086
## iter 50 value 12.679920
## iter 60 value 12.258777
## iter 70 value 12.175820
## iter 80 value 11.870675
## iter 90 value 11.801158
## iter 100 value 11.799397
## final value 11.799397
## stopped after 100 iterations
## # weights: 39
## initial value 105.880894
## iter 10 value 72.160537
## iter 20 value 62.226050
## iter 30 value 60.131566
## iter 40 value 58.574585
## iter 50 value 58.315643
## iter 60 value 58.310338
## iter 70 value 57.111820
## iter 80 value 55.738623
## iter 90 value 55.049281
## iter 100 value 53.858656
## final value 53.858656
## stopped after 100 iterations
## # weights: 109
## initial value 105.445966
## iter 10 value 24.748807
## iter 20 value 8.480186
## iter 30 value 5.052165
## iter 40 value 3.145882
## iter 50 value 3.107279
## iter 60 value 3.076821
## iter 70 value 0.411933
## iter 80 value 0.224631
## iter 90 value 0.204758
## iter 100 value 0.191662
## final value 0.191662
## stopped after 100 iterations
## # weights: 179
## initial value 109.204451
## iter 10 value 39.441681
## iter 20 value 17.093497
## iter 30 value 12.699110
## iter 40 value 11.690340
## iter 50 value 11.587461
## iter 60 value 11.526514
## iter 70 value 11.380504
## iter 80 value 11.260548
## iter 90 value 10.941939
## iter 100 value 10.445425
## final value 10.445425
## stopped after 100 iterations
## # weights: 39
## initial value 98.363151
## iter 10 value 67.624509
## iter 20 value 60.633832
## iter 30 value 57.193424
## iter 40 value 56.093446
## iter 50 value 55.973291
## iter 60 value 55.956924
## iter 70 value 55.955302
## iter 80 value 55.954574
## iter 90 value 55.954275
## iter 100 value 55.954048
## final value 55.954048
## stopped after 100 iterations
## # weights: 109
## initial value 103.419247
## iter 10 value 46.156053
## iter 20 value 23.139476
## iter 30 value 13.198848
## iter 40 value 10.844461
## iter 50 value 2.386110
## iter 60 value 0.088193
## iter 70 value 0.008547
## iter 80 value 0.005125
## iter 90 value 0.002214
## iter 100 value 0.000690
## final value 0.000690
## stopped after 100 iterations
## # weights: 179
## initial value 106.351747
## iter 10 value 35.788512
## iter 20 value 21.996247
## iter 30 value 19.139947
## iter 40 value 18.227055
## iter 50 value 16.182392
## iter 60 value 14.422546
## iter 70 value 13.888150
## iter 80 value 13.462837
## iter 90 value 13.165692
## iter 100 value 12.388587
## final value 12.388587
## stopped after 100 iterations
## # weights: 39
## initial value 101.909049
## iter 10 value 83.815337
## iter 20 value 61.818776
## iter 30 value 58.439010
## iter 40 value 56.635246
## iter 50 value 55.989602
## iter 60 value 55.965725
## final value 55.965721
## converged
## # weights: 109
## initial value 113.992190
## iter 10 value 34.746678
## iter 20 value 19.581461
## iter 30 value 16.901982
## iter 40 value 16.466751
## iter 50 value 16.430641
## iter 60 value 16.430493
## iter 60 value 16.430493
## iter 60 value 16.430493
## final value 16.430493
## converged
## # weights: 179
## initial value 101.122494
## iter 10 value 40.285028
## iter 20 value 19.781269
## iter 30 value 13.669962
## iter 40 value 12.997945
## iter 50 value 12.772024
## iter 60 value 12.757873
## iter 70 value 12.753326
## iter 80 value 12.750597
## iter 90 value 12.737905
## iter 100 value 12.735693
## final value 12.735693
## stopped after 100 iterations
## # weights: 39
## initial value 98.298796
## iter 10 value 61.422001
## iter 20 value 58.642248
## iter 30 value 58.065218
## iter 40 value 58.046885
## iter 50 value 57.865301
## iter 60 value 57.552258
## iter 70 value 57.350448
## iter 80 value 57.061957
## iter 90 value 56.647756
## iter 100 value 56.521578
## final value 56.521578
## stopped after 100 iterations
## # weights: 109
## initial value 109.441374
## iter 10 value 49.270731
## iter 20 value 26.177021
## iter 30 value 21.712290
## iter 40 value 18.950270
## iter 50 value 18.532337
## iter 60 value 17.944845
## iter 70 value 17.750275
## iter 80 value 16.764449
## iter 90 value 14.358135
## iter 100 value 14.257848
## final value 14.257848
## stopped after 100 iterations
## # weights: 179
## initial value 107.599108
## iter 10 value 31.782188
## iter 20 value 11.582940
## iter 30 value 9.534893
## iter 40 value 8.602916
## iter 50 value 8.186872
## iter 60 value 6.368664
## iter 70 value 6.169211
## iter 80 value 2.927200
## iter 90 value 2.902793
## iter 100 value 2.883550
## final value 2.883550
## stopped after 100 iterations
## # weights: 39
## initial value 105.743871
## iter 10 value 54.754192
## iter 20 value 51.203334
## iter 30 value 50.923013
## iter 40 value 50.526205
## iter 50 value 49.996766
## iter 60 value 49.701266
## iter 70 value 49.551492
## iter 80 value 48.614428
## iter 90 value 48.541854
## iter 100 value 48.532960
## final value 48.532960
## stopped after 100 iterations
## # weights: 109
## initial value 98.378575
## iter 10 value 35.522758
## iter 20 value 19.777993
## iter 30 value 13.389981
## iter 40 value 12.625373
## iter 50 value 12.556115
## iter 60 value 10.750257
## iter 70 value 10.589102
## iter 80 value 10.142654
## iter 90 value 8.250222
## iter 100 value 8.181177
## final value 8.181177
## stopped after 100 iterations
## # weights: 179
## initial value 113.392088
## iter 10 value 32.077971
## iter 20 value 17.442423
## iter 30 value 13.508386
## iter 40 value 9.728475
## iter 50 value 6.937029
## iter 60 value 5.787623
## iter 70 value 4.548593
## iter 80 value 4.276075
## iter 90 value 2.285914
## iter 100 value 2.017153
## final value 2.017153
## stopped after 100 iterations
## # weights: 39
## initial value 110.298436
## iter 10 value 70.831364
## iter 20 value 61.775637
## iter 30 value 57.984538
## iter 40 value 57.599174
## final value 57.592674
## converged
## # weights: 109
## initial value 101.927428
## iter 10 value 46.247144
## iter 20 value 22.521389
## iter 30 value 17.588709
## iter 40 value 16.488760
## iter 50 value 16.460482
## iter 60 value 16.459684
## iter 70 value 16.183518
## iter 80 value 16.137454
## iter 90 value 16.137075
## final value 16.137075
## converged
## # weights: 179
## initial value 124.797322
## iter 10 value 45.810230
## iter 20 value 24.971244
## iter 30 value 17.316854
## iter 40 value 14.727914
## iter 50 value 12.960653
## iter 60 value 12.373773
## iter 70 value 12.074334
## iter 80 value 11.976357
## iter 90 value 11.968884
## iter 100 value 11.967812
## final value 11.967812
## stopped after 100 iterations
## # weights: 39
## initial value 101.612215
## iter 10 value 64.843294
## iter 20 value 57.960247
## iter 30 value 53.378408
## iter 40 value 50.908074
## iter 50 value 48.054990
## iter 60 value 48.041764
## iter 70 value 48.038991
## iter 80 value 48.035420
## iter 90 value 48.034289
## iter 100 value 48.032511
## final value 48.032511
## stopped after 100 iterations
## # weights: 109
## initial value 128.191366
## iter 10 value 55.618530
## iter 20 value 41.772728
## iter 30 value 31.440104
## iter 40 value 29.638257
## iter 50 value 26.358345
## iter 60 value 25.134564
## iter 70 value 24.992664
## iter 80 value 24.682289
## iter 90 value 23.984477
## iter 100 value 19.869928
## final value 19.869928
## stopped after 100 iterations
## # weights: 179
## initial value 100.306791
## iter 10 value 26.609017
## iter 20 value 13.561397
## iter 30 value 11.641858
## iter 40 value 9.221786
## iter 50 value 7.792437
## iter 60 value 6.398928
## iter 70 value 5.003824
## iter 80 value 4.919280
## iter 90 value 4.908407
## iter 100 value 2.506795
## final value 2.506795
## stopped after 100 iterations
## # weights: 39
## initial value 102.553626
## iter 10 value 70.148006
## iter 20 value 67.830716
## iter 30 value 67.439081
## iter 40 value 67.093759
## iter 50 value 66.560196
## iter 60 value 64.630462
## iter 70 value 64.624002
## iter 80 value 64.060548
## iter 90 value 64.000883
## iter 100 value 63.992547
## final value 63.992547
## stopped after 100 iterations
## # weights: 109
## initial value 97.286293
## iter 10 value 20.656260
## iter 20 value 11.181246
## iter 30 value 3.458362
## iter 40 value 3.367512
## iter 50 value 3.365139
## iter 60 value 3.365060
## final value 3.365060
## converged
## # weights: 179
## initial value 132.619398
## iter 10 value 22.534098
## iter 20 value 8.917627
## iter 30 value 3.969384
## iter 40 value 3.892390
## iter 50 value 3.888532
## iter 60 value 3.888307
## final value 3.888307
## converged
## # weights: 39
## initial value 109.991995
## iter 10 value 69.334335
## iter 20 value 60.102021
## iter 30 value 56.653929
## iter 40 value 56.310702
## iter 50 value 56.001484
## final value 55.998821
## converged
## # weights: 109
## initial value 107.568757
## iter 10 value 44.422833
## iter 20 value 27.715355
## iter 30 value 19.698976
## iter 40 value 16.187469
## iter 50 value 15.878375
## iter 60 value 15.710391
## iter 70 value 15.675832
## final value 15.675699
## converged
## # weights: 179
## initial value 165.953703
## iter 10 value 61.975633
## iter 20 value 20.955199
## iter 30 value 13.015305
## iter 40 value 12.076087
## iter 50 value 11.885068
## iter 60 value 11.828839
## iter 70 value 11.781080
## iter 80 value 11.776055
## iter 90 value 11.775850
## iter 100 value 11.775827
## final value 11.775827
## stopped after 100 iterations
## # weights: 39
## initial value 108.794921
## iter 10 value 71.007891
## iter 20 value 68.856002
## iter 30 value 68.210033
## iter 40 value 68.076816
## iter 50 value 67.987203
## iter 60 value 67.950347
## iter 70 value 67.938321
## iter 80 value 67.928485
## iter 90 value 67.923458
## iter 100 value 67.877822
## final value 67.877822
## stopped after 100 iterations
## # weights: 109
## initial value 107.900894
## iter 10 value 40.841989
## iter 20 value 21.071311
## iter 30 value 13.768799
## iter 40 value 12.782516
## iter 50 value 12.716667
## iter 60 value 12.247329
## iter 70 value 10.990797
## iter 80 value 10.750290
## iter 90 value 10.680139
## iter 100 value 10.635746
## final value 10.635746
## stopped after 100 iterations
## # weights: 179
## initial value 112.823429
## iter 10 value 36.415183
## iter 20 value 22.613074
## iter 30 value 19.019331
## iter 40 value 17.745161
## iter 50 value 17.402077
## iter 60 value 16.645010
## iter 70 value 15.978487
## iter 80 value 15.624066
## iter 90 value 15.607758
## iter 100 value 15.592408
## final value 15.592408
## stopped after 100 iterations
## # weights: 39
## initial value 108.629768
## iter 10 value 67.402471
## iter 20 value 58.695268
## iter 30 value 53.511530
## iter 40 value 49.684811
## iter 50 value 48.739661
## iter 60 value 48.677244
## iter 70 value 47.737636
## iter 80 value 47.194317
## iter 90 value 46.748951
## iter 100 value 46.642830
## final value 46.642830
## stopped after 100 iterations
## # weights: 109
## initial value 113.450153
## iter 10 value 46.638019
## iter 20 value 29.465079
## iter 30 value 27.548786
## iter 40 value 24.093047
## iter 50 value 22.343031
## iter 60 value 20.080774
## iter 70 value 15.199952
## iter 80 value 12.461883
## iter 90 value 12.289956
## iter 100 value 10.404381
## final value 10.404381
## stopped after 100 iterations
## # weights: 179
## initial value 113.906049
## iter 10 value 34.327061
## iter 20 value 5.716226
## iter 30 value 2.428107
## iter 40 value 1.931393
## iter 50 value 1.910801
## iter 60 value 1.909631
## iter 70 value 1.908669
## iter 80 value 1.907202
## iter 90 value 1.386891
## final value 1.386310
## converged
## # weights: 39
## initial value 122.529973
## iter 10 value 79.396166
## iter 20 value 64.985604
## iter 30 value 62.206387
## iter 40 value 60.081526
## iter 50 value 59.708559
## final value 59.706366
## converged
## # weights: 109
## initial value 110.888675
## iter 10 value 54.470268
## iter 20 value 28.999347
## iter 30 value 20.760679
## iter 40 value 18.376026
## iter 50 value 16.044729
## iter 60 value 15.885068
## iter 70 value 15.884350
## final value 15.884347
## converged
## # weights: 179
## initial value 128.878174
## iter 10 value 56.205250
## iter 20 value 24.379722
## iter 30 value 16.468165
## iter 40 value 13.774095
## iter 50 value 13.161681
## iter 60 value 13.022688
## iter 70 value 12.972723
## iter 80 value 12.865362
## iter 90 value 12.769547
## iter 100 value 12.621778
## final value 12.621778
## stopped after 100 iterations
## # weights: 39
## initial value 112.224905
## iter 10 value 61.512202
## iter 20 value 56.892719
## iter 30 value 55.881156
## iter 40 value 55.852551
## iter 50 value 54.757422
## iter 60 value 52.780134
## iter 70 value 52.379027
## iter 80 value 52.361468
## iter 90 value 52.351718
## iter 100 value 52.349046
## final value 52.349046
## stopped after 100 iterations
## # weights: 109
## initial value 114.821735
## iter 10 value 35.898037
## iter 20 value 15.665394
## iter 30 value 13.078620
## iter 40 value 12.855718
## iter 50 value 12.697503
## iter 60 value 12.292885
## iter 70 value 12.177201
## iter 80 value 12.150601
## iter 90 value 12.114948
## iter 100 value 12.090408
## final value 12.090408
## stopped after 100 iterations
## # weights: 179
## initial value 112.748959
## iter 10 value 28.986174
## iter 20 value 8.680665
## iter 30 value 3.286974
## iter 40 value 3.007362
## iter 50 value 2.944428
## iter 60 value 2.938071
## iter 70 value 2.927772
## iter 80 value 2.870394
## iter 90 value 2.055326
## iter 100 value 2.048359
## final value 2.048359
## stopped after 100 iterations
## # weights: 39
## initial value 95.850764
## iter 10 value 62.453250
## iter 20 value 57.903188
## iter 30 value 50.615236
## iter 40 value 47.879002
## iter 50 value 47.216466
## iter 60 value 47.167444
## iter 70 value 46.881449
## iter 80 value 45.768182
## iter 90 value 45.758869
## iter 100 value 44.747057
## final value 44.747057
## stopped after 100 iterations
## # weights: 109
## initial value 115.113788
## iter 10 value 35.844141
## iter 20 value 18.404776
## iter 30 value 16.750748
## iter 40 value 16.243209
## iter 50 value 16.191514
## iter 60 value 16.188828
## iter 70 value 16.188421
## iter 80 value 16.188386
## final value 16.188378
## converged
## # weights: 179
## initial value 98.513063
## iter 10 value 11.027990
## iter 20 value 0.375485
## iter 30 value 0.003338
## final value 0.000063
## converged
## # weights: 39
## initial value 98.246345
## iter 10 value 75.637567
## iter 20 value 62.043113
## iter 30 value 61.098588
## iter 40 value 60.071421
## iter 50 value 57.564738
## iter 60 value 57.131452
## iter 70 value 56.347132
## iter 80 value 55.781382
## iter 90 value 55.752338
## final value 55.752265
## converged
## # weights: 109
## initial value 97.917261
## iter 10 value 49.815846
## iter 20 value 29.049349
## iter 30 value 18.481692
## iter 40 value 16.472866
## iter 50 value 16.343455
## iter 60 value 16.341053
## iter 70 value 16.341018
## iter 70 value 16.341018
## iter 70 value 16.341018
## final value 16.341018
## converged
## # weights: 179
## initial value 116.019447
## iter 10 value 34.449699
## iter 20 value 16.765738
## iter 30 value 12.384215
## iter 40 value 11.945279
## iter 50 value 11.793083
## iter 60 value 11.659182
## iter 70 value 11.654419
## iter 80 value 11.653949
## iter 90 value 11.653946
## final value 11.653945
## converged
## # weights: 39
## initial value 97.285276
## iter 10 value 56.132521
## iter 20 value 53.182804
## iter 30 value 52.474062
## iter 40 value 51.545756
## iter 50 value 51.254311
## iter 60 value 50.255686
## iter 70 value 48.494321
## iter 80 value 44.747946
## iter 90 value 42.099681
## iter 100 value 41.183351
## final value 41.183351
## stopped after 100 iterations
## # weights: 109
## initial value 110.367790
## iter 10 value 32.758854
## iter 20 value 24.553723
## iter 30 value 22.242126
## iter 40 value 18.200096
## iter 50 value 16.305141
## iter 60 value 14.531900
## iter 70 value 14.121520
## iter 80 value 13.471591
## iter 90 value 13.445634
## iter 100 value 13.439581
## final value 13.439581
## stopped after 100 iterations
## # weights: 179
## initial value 103.522266
## iter 10 value 24.327191
## iter 20 value 11.539824
## iter 30 value 5.156288
## iter 40 value 3.623094
## iter 50 value 3.258195
## iter 60 value 2.958701
## iter 70 value 2.387416
## iter 80 value 2.243098
## iter 90 value 2.201411
## iter 100 value 2.153700
## final value 2.153700
## stopped after 100 iterations
## # weights: 39
## initial value 104.273895
## iter 10 value 80.178196
## iter 20 value 66.431267
## iter 30 value 63.061843
## iter 40 value 59.589535
## iter 50 value 56.644395
## iter 60 value 53.806732
## iter 70 value 53.400959
## iter 80 value 53.360288
## iter 90 value 53.343806
## iter 100 value 53.340406
## final value 53.340406
## stopped after 100 iterations
## # weights: 109
## initial value 104.200368
## iter 10 value 21.243159
## iter 20 value 14.672576
## iter 30 value 13.342334
## iter 40 value 12.862696
## iter 50 value 12.394133
## iter 60 value 12.358623
## iter 70 value 12.247926
## iter 80 value 12.200178
## iter 90 value 12.187596
## iter 100 value 11.548474
## final value 11.548474
## stopped after 100 iterations
## # weights: 179
## initial value 111.120113
## iter 10 value 37.173051
## iter 20 value 13.196762
## iter 30 value 5.858393
## iter 40 value 3.104145
## iter 50 value 0.864227
## iter 60 value 0.274956
## iter 70 value 0.087390
## iter 80 value 0.016942
## iter 90 value 0.002319
## iter 100 value 0.000615
## final value 0.000615
## stopped after 100 iterations
## # weights: 39
## initial value 104.685204
## iter 10 value 75.140028
## iter 20 value 65.893640
## iter 30 value 64.216871
## iter 40 value 61.255605
## iter 50 value 59.087264
## iter 60 value 58.293563
## iter 70 value 58.015434
## iter 80 value 57.571189
## iter 90 value 57.133244
## iter 100 value 57.114779
## final value 57.114779
## stopped after 100 iterations
## # weights: 109
## initial value 105.860992
## iter 10 value 52.608029
## iter 20 value 26.844929
## iter 30 value 18.915280
## iter 40 value 16.107803
## iter 50 value 15.697892
## iter 60 value 15.682900
## iter 70 value 15.682415
## final value 15.682415
## converged
## # weights: 179
## initial value 100.267651
## iter 10 value 26.444931
## iter 20 value 16.495048
## iter 30 value 14.037741
## iter 40 value 13.412082
## iter 50 value 12.767397
## iter 60 value 12.364658
## iter 70 value 12.289889
## iter 80 value 12.274146
## iter 90 value 11.989018
## iter 100 value 11.937742
## final value 11.937742
## stopped after 100 iterations
## # weights: 39
## initial value 109.747464
## iter 10 value 56.331905
## iter 20 value 49.041478
## iter 30 value 46.397400
## iter 40 value 45.541175
## iter 50 value 45.428697
## iter 60 value 44.929897
## iter 70 value 44.795721
## iter 80 value 44.595495
## iter 90 value 44.015911
## iter 100 value 43.945279
## final value 43.945279
## stopped after 100 iterations
## # weights: 109
## initial value 113.605104
## iter 10 value 43.466165
## iter 20 value 13.322778
## iter 30 value 12.275673
## iter 40 value 3.683058
## iter 50 value 3.018265
## iter 60 value 2.891851
## iter 70 value 2.710816
## iter 80 value 2.667923
## iter 90 value 2.652158
## iter 100 value 1.528409
## final value 1.528409
## stopped after 100 iterations
## # weights: 179
## initial value 106.179434
## iter 10 value 20.801837
## iter 20 value 2.620206
## iter 30 value 0.302520
## iter 40 value 0.250760
## iter 50 value 0.228579
## iter 60 value 0.215545
## iter 70 value 0.203794
## iter 80 value 0.192145
## iter 90 value 0.184748
## iter 100 value 0.171013
## final value 0.171013
## stopped after 100 iterations
## # weights: 39
## initial value 100.973925
## iter 10 value 57.784520
## iter 20 value 52.654989
## iter 30 value 50.229106
## iter 40 value 50.017123
## iter 50 value 49.974248
## iter 60 value 49.956866
## iter 70 value 49.940940
## iter 80 value 49.937328
## iter 90 value 49.936365
## iter 100 value 49.925771
## final value 49.925771
## stopped after 100 iterations
## # weights: 109
## initial value 105.825365
## iter 10 value 76.407707
## iter 20 value 22.095252
## iter 30 value 18.100752
## iter 40 value 17.654298
## iter 50 value 17.178281
## iter 60 value 16.967075
## iter 70 value 16.949367
## iter 80 value 16.562687
## iter 90 value 15.824019
## iter 100 value 15.411064
## final value 15.411064
## stopped after 100 iterations
## # weights: 179
## initial value 142.362018
## iter 10 value 30.525406
## iter 20 value 15.346714
## iter 30 value 9.294052
## iter 40 value 8.462412
## iter 50 value 8.252965
## iter 60 value 7.934935
## iter 70 value 6.902517
## iter 80 value 5.465348
## iter 90 value 5.415021
## iter 100 value 5.398348
## final value 5.398348
## stopped after 100 iterations
## # weights: 39
## initial value 100.160587
## iter 10 value 76.431954
## iter 20 value 64.302241
## iter 30 value 60.977127
## iter 40 value 59.615263
## iter 50 value 58.452224
## iter 60 value 58.296665
## final value 58.296647
## converged
## # weights: 109
## initial value 103.178689
## iter 10 value 55.083940
## iter 20 value 35.274241
## iter 30 value 21.559043
## iter 40 value 16.599123
## iter 50 value 16.115686
## iter 60 value 15.885448
## iter 70 value 15.668320
## iter 80 value 15.658130
## final value 15.658085
## converged
## # weights: 179
## initial value 119.207751
## iter 10 value 35.043853
## iter 20 value 19.259180
## iter 30 value 14.215892
## iter 40 value 13.225754
## iter 50 value 12.315779
## iter 60 value 12.073245
## iter 70 value 12.054459
## iter 80 value 12.053437
## final value 12.053428
## converged
## # weights: 39
## initial value 102.870859
## iter 10 value 65.530454
## iter 20 value 57.153446
## iter 30 value 51.913187
## iter 40 value 51.479502
## iter 50 value 51.473677
## iter 60 value 51.472720
## iter 70 value 51.471604
## iter 80 value 51.463795
## iter 90 value 50.363243
## iter 100 value 46.940685
## final value 46.940685
## stopped after 100 iterations
## # weights: 109
## initial value 106.702826
## iter 10 value 39.530385
## iter 20 value 31.274918
## iter 30 value 28.143945
## iter 40 value 28.086041
## iter 50 value 28.029798
## iter 60 value 28.011869
## iter 70 value 27.901559
## iter 80 value 27.489760
## iter 90 value 24.101177
## iter 100 value 23.644284
## final value 23.644284
## stopped after 100 iterations
## # weights: 179
## initial value 131.102634
## iter 10 value 33.895810
## iter 20 value 15.953847
## iter 30 value 1.198418
## iter 40 value 0.281151
## iter 50 value 0.245688
## iter 60 value 0.220019
## iter 70 value 0.197471
## iter 80 value 0.173831
## iter 90 value 0.160837
## iter 100 value 0.145285
## final value 0.145285
## stopped after 100 iterations
## # weights: 39
## initial value 103.592126
## iter 10 value 79.208572
## iter 20 value 73.755657
## iter 30 value 72.896207
## iter 40 value 71.964296
## iter 50 value 71.126707
## iter 60 value 70.989323
## iter 70 value 70.785329
## iter 80 value 70.698563
## iter 90 value 69.189643
## iter 100 value 68.510562
## final value 68.510562
## stopped after 100 iterations
## # weights: 109
## initial value 106.964333
## iter 10 value 44.649207
## iter 20 value 21.816080
## iter 30 value 15.075303
## iter 40 value 14.089928
## iter 50 value 13.680059
## iter 60 value 13.633925
## iter 70 value 13.627821
## iter 80 value 13.627396
## iter 90 value 13.627041
## iter 100 value 13.620659
## final value 13.620659
## stopped after 100 iterations
## # weights: 179
## initial value 141.144873
## iter 10 value 54.513365
## iter 20 value 31.163049
## iter 30 value 18.840029
## iter 40 value 6.466062
## iter 50 value 0.491646
## iter 60 value 0.133271
## iter 70 value 0.029541
## iter 80 value 0.007058
## iter 90 value 0.002955
## iter 100 value 0.000949
## final value 0.000949
## stopped after 100 iterations
## # weights: 39
## initial value 100.055874
## iter 10 value 73.232858
## iter 20 value 64.678985
## iter 30 value 61.608996
## iter 40 value 56.456073
## iter 50 value 55.024790
## iter 60 value 54.999220
## final value 54.999116
## converged
## # weights: 109
## initial value 101.514721
## iter 10 value 45.969373
## iter 20 value 26.365913
## iter 30 value 21.266877
## iter 40 value 16.977484
## iter 50 value 16.230839
## iter 60 value 16.217339
## iter 70 value 16.209484
## iter 80 value 16.187465
## iter 90 value 16.169314
## iter 100 value 16.168323
## final value 16.168323
## stopped after 100 iterations
## # weights: 179
## initial value 104.303083
## iter 10 value 35.011912
## iter 20 value 19.831508
## iter 30 value 14.055075
## iter 40 value 12.933150
## iter 50 value 12.813755
## iter 60 value 12.306663
## iter 70 value 11.898151
## iter 80 value 11.837618
## iter 90 value 11.837181
## final value 11.837157
## converged
## # weights: 39
## initial value 96.801867
## iter 10 value 63.620322
## iter 20 value 57.892841
## iter 30 value 56.838873
## iter 40 value 56.219903
## iter 50 value 53.626095
## iter 60 value 49.140965
## iter 70 value 44.667961
## iter 80 value 40.556063
## iter 90 value 36.705894
## iter 100 value 36.003240
## final value 36.003240
## stopped after 100 iterations
## # weights: 109
## initial value 109.479868
## iter 10 value 32.572522
## iter 20 value 19.541014
## iter 30 value 18.301975
## iter 40 value 18.104545
## iter 50 value 18.074769
## iter 60 value 18.059927
## iter 70 value 17.614126
## iter 80 value 17.574032
## iter 90 value 16.046639
## iter 100 value 12.460793
## final value 12.460793
## stopped after 100 iterations
## # weights: 179
## initial value 111.920541
## iter 10 value 37.578512
## iter 20 value 12.645302
## iter 30 value 11.388523
## iter 40 value 9.620623
## iter 50 value 9.098781
## iter 60 value 8.992492
## iter 70 value 8.891099
## iter 80 value 8.697371
## iter 90 value 8.505899
## iter 100 value 5.382842
## final value 5.382842
## stopped after 100 iterations
## # weights: 39
## initial value 111.713833
## iter 10 value 79.246710
## iter 20 value 57.860666
## iter 30 value 56.787143
## iter 40 value 56.250428
## iter 50 value 55.686564
## iter 60 value 54.384607
## iter 70 value 53.267637
## iter 80 value 53.012650
## iter 90 value 51.101749
## iter 100 value 51.022758
## final value 51.022758
## stopped after 100 iterations
## # weights: 109
## initial value 120.647719
## iter 10 value 63.132511
## iter 20 value 36.602502
## iter 30 value 26.648291
## iter 40 value 23.688396
## iter 50 value 21.632804
## iter 60 value 21.408187
## iter 70 value 21.132168
## iter 80 value 20.455011
## iter 90 value 19.733412
## iter 100 value 17.952214
## final value 17.952214
## stopped after 100 iterations
## # weights: 179
## initial value 104.277310
## iter 10 value 23.973221
## iter 20 value 4.155488
## iter 30 value 2.297378
## iter 40 value 2.250008
## iter 50 value 2.249362
## final value 2.249356
## converged
## # weights: 39
## initial value 102.263714
## iter 10 value 72.966869
## iter 20 value 63.939617
## iter 30 value 57.829239
## iter 40 value 56.970629
## iter 50 value 56.742280
## iter 60 value 56.326926
## iter 70 value 55.708456
## iter 80 value 55.686515
## final value 55.686510
## converged
## # weights: 109
## initial value 110.621111
## iter 10 value 42.607705
## iter 20 value 22.965918
## iter 30 value 17.436947
## iter 40 value 16.868241
## iter 50 value 16.831300
## iter 60 value 16.829190
## iter 70 value 16.829109
## final value 16.829108
## converged
## # weights: 179
## initial value 136.559433
## iter 10 value 47.009197
## iter 20 value 26.503933
## iter 30 value 17.023482
## iter 40 value 13.653284
## iter 50 value 12.781369
## iter 60 value 12.629725
## iter 70 value 12.604546
## iter 80 value 12.602509
## iter 90 value 12.579541
## iter 100 value 12.572201
## final value 12.572201
## stopped after 100 iterations
## # weights: 39
## initial value 106.054116
## iter 10 value 68.605511
## iter 20 value 62.390504
## iter 30 value 60.442573
## iter 40 value 59.435690
## iter 50 value 54.404426
## iter 60 value 50.285366
## iter 70 value 48.798674
## iter 80 value 48.393695
## iter 90 value 47.996002
## iter 100 value 47.527822
## final value 47.527822
## stopped after 100 iterations
## # weights: 109
## initial value 112.321504
## iter 10 value 47.353748
## iter 20 value 26.897734
## iter 30 value 15.043379
## iter 40 value 12.644512
## iter 50 value 11.955071
## iter 60 value 11.666453
## iter 70 value 11.253217
## iter 80 value 10.966730
## iter 90 value 9.490496
## iter 100 value 9.229535
## final value 9.229535
## stopped after 100 iterations
## # weights: 179
## initial value 102.179906
## iter 10 value 18.568774
## iter 20 value 3.508090
## iter 30 value 0.655172
## iter 40 value 0.185853
## iter 50 value 0.167920
## iter 60 value 0.159749
## iter 70 value 0.144850
## iter 80 value 0.132991
## iter 90 value 0.117110
## iter 100 value 0.106325
## final value 0.106325
## stopped after 100 iterations
## # weights: 39
## initial value 111.480998
## iter 10 value 78.242925
## iter 20 value 73.444508
## iter 30 value 69.506263
## iter 40 value 68.014797
## iter 50 value 67.849233
## iter 60 value 67.828279
## iter 70 value 67.825893
## iter 80 value 67.825610
## iter 90 value 67.825400
## final value 67.825337
## converged
## # weights: 109
## initial value 114.175458
## iter 10 value 50.689604
## iter 20 value 37.328890
## iter 30 value 31.590767
## iter 40 value 31.457850
## iter 50 value 31.296343
## iter 60 value 31.211943
## iter 70 value 31.160221
## iter 80 value 31.143343
## iter 90 value 31.119216
## iter 100 value 31.055241
## final value 31.055241
## stopped after 100 iterations
## # weights: 179
## initial value 136.031181
## iter 10 value 29.466597
## iter 20 value 15.446634
## iter 30 value 11.987605
## iter 40 value 9.698625
## iter 50 value 7.126793
## iter 60 value 6.082322
## iter 70 value 5.846941
## iter 80 value 5.625418
## iter 90 value 5.378058
## iter 100 value 4.618173
## final value 4.618173
## stopped after 100 iterations
## # weights: 39
## initial value 108.610709
## iter 10 value 92.176523
## iter 20 value 70.321851
## iter 30 value 62.516397
## iter 40 value 61.273200
## iter 50 value 61.262374
## final value 61.262364
## converged
## # weights: 109
## initial value 142.176533
## iter 10 value 75.274168
## iter 20 value 47.143641
## iter 30 value 34.925998
## iter 40 value 22.069397
## iter 50 value 17.370121
## iter 60 value 16.839118
## iter 70 value 16.697304
## iter 80 value 16.679119
## iter 90 value 16.678242
## iter 100 value 16.678227
## final value 16.678227
## stopped after 100 iterations
## # weights: 179
## initial value 114.377077
## iter 10 value 39.244631
## iter 20 value 18.233864
## iter 30 value 15.055457
## iter 40 value 13.744873
## iter 50 value 13.089238
## iter 60 value 12.959814
## iter 70 value 12.677389
## iter 80 value 12.619670
## iter 90 value 12.618037
## final value 12.618011
## converged
## # weights: 39
## initial value 107.724661
## iter 10 value 71.430965
## iter 20 value 66.711827
## iter 30 value 65.020372
## iter 40 value 64.229349
## iter 50 value 64.141627
## iter 60 value 61.321386
## iter 70 value 60.498812
## iter 80 value 60.094100
## iter 90 value 59.406336
## iter 100 value 58.950842
## final value 58.950842
## stopped after 100 iterations
## # weights: 109
## initial value 108.904984
## iter 10 value 45.818045
## iter 20 value 27.374777
## iter 30 value 15.843102
## iter 40 value 13.368509
## iter 50 value 11.419307
## iter 60 value 11.068135
## iter 70 value 11.043391
## iter 80 value 10.993268
## iter 90 value 10.783770
## iter 100 value 10.635422
## final value 10.635422
## stopped after 100 iterations
## # weights: 179
## initial value 105.547978
## iter 10 value 24.652374
## iter 20 value 1.721313
## iter 30 value 0.317535
## iter 40 value 0.284089
## iter 50 value 0.245125
## iter 60 value 0.226665
## iter 70 value 0.197506
## iter 80 value 0.179815
## iter 90 value 0.160319
## iter 100 value 0.142928
## final value 0.142928
## stopped after 100 iterations
## # weights: 39
## initial value 120.645515
## iter 10 value 61.637485
## iter 20 value 53.123121
## iter 30 value 48.896885
## iter 40 value 48.715487
## iter 50 value 48.714829
## iter 60 value 48.714706
## iter 60 value 48.714706
## iter 60 value 48.714706
## final value 48.714706
## converged
## # weights: 109
## initial value 106.861698
## iter 10 value 22.601379
## iter 20 value 10.206481
## iter 30 value 9.499900
## iter 40 value 9.433939
## iter 50 value 9.424377
## iter 60 value 9.366633
## iter 70 value 9.326111
## iter 80 value 9.272575
## iter 90 value 9.260439
## iter 100 value 9.222738
## final value 9.222738
## stopped after 100 iterations
## # weights: 179
## initial value 98.938722
## iter 10 value 17.703871
## iter 20 value 9.831781
## iter 30 value 6.229459
## iter 40 value 5.584642
## iter 50 value 5.555802
## iter 60 value 3.320219
## iter 70 value 3.293536
## iter 80 value 3.167083
## iter 90 value 3.148088
## iter 100 value 3.143881
## final value 3.143881
## stopped after 100 iterations
## # weights: 39
## initial value 109.335008
## iter 10 value 79.676130
## iter 20 value 66.874308
## iter 30 value 62.342386
## iter 40 value 61.042049
## iter 50 value 59.267360
## iter 60 value 58.817616
## iter 70 value 58.812712
## final value 58.812628
## converged
## # weights: 109
## initial value 145.111699
## iter 10 value 86.303677
## iter 20 value 48.857213
## iter 30 value 25.180623
## iter 40 value 17.647503
## iter 50 value 16.928812
## iter 60 value 16.404424
## iter 70 value 16.274916
## iter 80 value 16.247700
## iter 90 value 16.237666
## iter 100 value 16.233934
## final value 16.233934
## stopped after 100 iterations
## # weights: 179
## initial value 120.650479
## iter 10 value 35.934519
## iter 20 value 18.102816
## iter 30 value 14.347485
## iter 40 value 12.788833
## iter 50 value 12.095441
## iter 60 value 11.909278
## iter 70 value 11.804437
## iter 80 value 11.793172
## iter 90 value 11.792811
## iter 100 value 11.792798
## final value 11.792798
## stopped after 100 iterations
## # weights: 39
## initial value 105.058638
## iter 10 value 78.685765
## iter 20 value 61.916126
## iter 30 value 59.164554
## iter 40 value 59.156549
## iter 50 value 59.109079
## iter 60 value 58.005356
## iter 70 value 57.790018
## iter 80 value 57.758283
## iter 90 value 57.725369
## iter 100 value 55.805571
## final value 55.805571
## stopped after 100 iterations
## # weights: 109
## initial value 100.919183
## iter 10 value 46.992471
## iter 20 value 8.827525
## iter 30 value 8.555688
## iter 40 value 8.535719
## iter 50 value 8.516062
## iter 60 value 8.502384
## iter 70 value 8.491016
## iter 80 value 8.483104
## iter 90 value 8.477313
## iter 100 value 8.471854
## final value 8.471854
## stopped after 100 iterations
## # weights: 179
## initial value 103.582615
## iter 10 value 28.582405
## iter 20 value 9.640441
## iter 30 value 6.065557
## iter 40 value 5.002357
## iter 50 value 4.290510
## iter 60 value 4.179200
## iter 70 value 4.111337
## iter 80 value 4.065425
## iter 90 value 4.041556
## iter 100 value 4.032525
## final value 4.032525
## stopped after 100 iterations
## # weights: 39
## initial value 101.782962
## iter 10 value 63.538181
## iter 20 value 57.534621
## iter 30 value 52.627096
## iter 40 value 51.794453
## iter 50 value 51.261330
## iter 60 value 51.258879
## iter 70 value 50.643050
## iter 80 value 50.086128
## iter 90 value 50.083170
## iter 100 value 50.079993
## final value 50.079993
## stopped after 100 iterations
## # weights: 109
## initial value 129.743763
## iter 10 value 37.640433
## iter 20 value 25.819287
## iter 30 value 21.980830
## iter 40 value 20.449987
## iter 50 value 19.446399
## iter 60 value 18.713876
## iter 70 value 17.638092
## iter 80 value 14.347201
## iter 90 value 14.067092
## iter 100 value 13.612554
## final value 13.612554
## stopped after 100 iterations
## # weights: 179
## initial value 105.748942
## iter 10 value 52.283717
## iter 20 value 12.942157
## iter 30 value 9.968846
## iter 40 value 8.587180
## iter 50 value 7.526429
## iter 60 value 5.244329
## iter 70 value 2.426233
## iter 80 value 0.158527
## iter 90 value 0.045803
## iter 100 value 0.022936
## final value 0.022936
## stopped after 100 iterations
## # weights: 39
## initial value 107.914127
## iter 10 value 75.450615
## iter 20 value 62.906812
## iter 30 value 60.135136
## iter 40 value 57.707300
## iter 50 value 57.538580
## iter 60 value 57.025204
## iter 70 value 56.121134
## iter 80 value 55.252132
## iter 90 value 54.213145
## final value 54.207165
## converged
## # weights: 109
## initial value 112.021578
## iter 10 value 53.765593
## iter 20 value 29.344986
## iter 30 value 19.989816
## iter 40 value 16.226425
## iter 50 value 15.447023
## iter 60 value 15.249666
## iter 70 value 15.247884
## iter 80 value 15.247825
## final value 15.247824
## converged
## # weights: 179
## initial value 100.916432
## iter 10 value 29.115380
## iter 20 value 15.897803
## iter 30 value 13.455035
## iter 40 value 12.771820
## iter 50 value 12.035579
## iter 60 value 11.874778
## iter 70 value 11.860929
## iter 80 value 11.860628
## final value 11.860625
## converged
## # weights: 39
## initial value 109.489056
## iter 10 value 79.237084
## iter 20 value 56.231497
## iter 30 value 53.489126
## iter 40 value 51.955538
## iter 50 value 51.353480
## iter 60 value 50.027010
## iter 70 value 49.199211
## iter 80 value 48.746208
## iter 90 value 48.585407
## iter 100 value 47.740760
## final value 47.740760
## stopped after 100 iterations
## # weights: 109
## initial value 114.920171
## iter 10 value 35.067807
## iter 20 value 22.209082
## iter 30 value 21.535110
## iter 40 value 18.112931
## iter 50 value 17.587471
## iter 60 value 17.548903
## iter 70 value 17.539604
## iter 80 value 17.525455
## iter 90 value 17.512475
## iter 100 value 17.500748
## final value 17.500748
## stopped after 100 iterations
## # weights: 179
## initial value 114.293964
## iter 10 value 20.980641
## iter 20 value 7.444786
## iter 30 value 5.627557
## iter 40 value 5.029257
## iter 50 value 3.018262
## iter 60 value 2.643045
## iter 70 value 2.583162
## iter 80 value 2.448768
## iter 90 value 0.403540
## iter 100 value 0.319113
## final value 0.319113
## stopped after 100 iterations
## # weights: 39
## initial value 104.998400
## iter 10 value 65.387418
## iter 20 value 55.477505
## iter 30 value 52.459363
## iter 40 value 49.830522
## iter 50 value 47.659868
## iter 60 value 46.665754
## iter 70 value 46.369165
## iter 80 value 46.352326
## iter 90 value 46.329716
## iter 100 value 46.006090
## final value 46.006090
## stopped after 100 iterations
## # weights: 109
## initial value 102.743289
## iter 10 value 30.690013
## iter 20 value 20.372395
## iter 30 value 18.373636
## iter 40 value 15.410071
## iter 50 value 14.657805
## iter 60 value 8.496365
## iter 70 value 7.812179
## iter 80 value 7.075790
## iter 90 value 7.022297
## iter 100 value 6.768809
## final value 6.768809
## stopped after 100 iterations
## # weights: 179
## initial value 113.419119
## iter 10 value 15.849269
## iter 20 value 0.460053
## iter 30 value 0.002249
## final value 0.000091
## converged
## # weights: 39
## initial value 109.775895
## iter 10 value 82.341835
## iter 20 value 72.463490
## iter 30 value 68.560593
## iter 40 value 63.323000
## iter 50 value 59.900773
## iter 60 value 56.980065
## iter 70 value 56.732388
## final value 56.732046
## converged
## # weights: 109
## initial value 107.624634
## iter 10 value 60.097713
## iter 20 value 31.943296
## iter 30 value 20.570084
## iter 40 value 17.380692
## iter 50 value 17.148130
## iter 60 value 17.143968
## final value 17.143850
## converged
## # weights: 179
## initial value 113.279239
## iter 10 value 31.886952
## iter 20 value 17.598580
## iter 30 value 14.606289
## iter 40 value 12.420506
## iter 50 value 12.121150
## iter 60 value 11.972272
## iter 70 value 11.961330
## iter 80 value 11.960746
## iter 90 value 11.960684
## final value 11.960682
## converged
## # weights: 39
## initial value 104.642163
## iter 10 value 80.124375
## iter 20 value 68.035844
## iter 30 value 63.527875
## iter 40 value 61.474930
## iter 50 value 58.870838
## iter 60 value 58.119046
## iter 70 value 58.111727
## iter 80 value 58.081951
## iter 90 value 58.059267
## iter 100 value 58.056581
## final value 58.056581
## stopped after 100 iterations
## # weights: 109
## initial value 118.713302
## iter 10 value 35.495847
## iter 20 value 23.053432
## iter 30 value 17.678575
## iter 40 value 13.383633
## iter 50 value 6.403845
## iter 60 value 5.771216
## iter 70 value 5.146710
## iter 80 value 5.104529
## iter 90 value 5.074264
## iter 100 value 5.050892
## final value 5.050892
## stopped after 100 iterations
## # weights: 179
## initial value 136.136052
## iter 10 value 46.161538
## iter 20 value 32.615980
## iter 30 value 28.986056
## iter 40 value 27.673266
## iter 50 value 24.785605
## iter 60 value 22.698249
## iter 70 value 21.604710
## iter 80 value 21.483422
## iter 90 value 21.324813
## iter 100 value 21.203244
## final value 21.203244
## stopped after 100 iterations
## # weights: 39
## initial value 113.350016
## iter 10 value 70.729232
## iter 20 value 62.349199
## iter 30 value 59.609717
## iter 40 value 59.014316
## iter 50 value 57.435928
## iter 60 value 54.055970
## iter 70 value 53.599324
## iter 80 value 53.577901
## iter 90 value 53.560967
## iter 100 value 53.479627
## final value 53.479627
## stopped after 100 iterations
## # weights: 109
## initial value 104.944288
## iter 10 value 30.882796
## iter 20 value 11.783503
## iter 30 value 8.486227
## iter 40 value 7.040081
## iter 50 value 7.038859
## iter 60 value 6.990009
## iter 70 value 6.968175
## iter 80 value 6.968151
## iter 90 value 6.968089
## iter 100 value 6.968057
## final value 6.968057
## stopped after 100 iterations
## # weights: 179
## initial value 112.800098
## iter 10 value 26.388570
## iter 20 value 5.599708
## iter 30 value 1.552520
## iter 40 value 1.399780
## iter 50 value 1.386405
## iter 60 value 1.386349
## iter 70 value 1.386321
## iter 80 value 1.386313
## final value 1.386312
## converged
## # weights: 39
## initial value 112.669379
## iter 10 value 93.971481
## iter 20 value 77.797487
## iter 30 value 66.077188
## iter 40 value 62.868417
## iter 50 value 60.977595
## iter 60 value 59.630071
## iter 70 value 59.126414
## iter 80 value 58.866708
## iter 90 value 58.675459
## iter 100 value 58.462103
## final value 58.462103
## stopped after 100 iterations
## # weights: 109
## initial value 97.018452
## iter 10 value 55.793926
## iter 20 value 30.563067
## iter 30 value 19.111092
## iter 40 value 17.362120
## iter 50 value 17.196488
## iter 60 value 17.187790
## iter 70 value 17.187614
## final value 17.187614
## converged
## # weights: 179
## initial value 110.573094
## iter 10 value 41.750471
## iter 20 value 22.404796
## iter 30 value 14.663886
## iter 40 value 12.767774
## iter 50 value 12.070396
## iter 60 value 11.977927
## iter 70 value 11.968827
## iter 80 value 11.968221
## final value 11.968218
## converged
## # weights: 39
## initial value 102.668271
## iter 10 value 76.222737
## iter 20 value 71.120357
## iter 30 value 70.002522
## iter 40 value 66.179117
## iter 50 value 63.429916
## iter 60 value 62.316294
## iter 70 value 61.335220
## iter 80 value 59.783985
## iter 90 value 59.404063
## iter 100 value 59.345563
## final value 59.345563
## stopped after 100 iterations
## # weights: 109
## initial value 106.120849
## iter 10 value 32.403149
## iter 20 value 18.474979
## iter 30 value 13.088859
## iter 40 value 11.447226
## iter 50 value 8.970656
## iter 60 value 8.864178
## iter 70 value 8.804195
## iter 80 value 8.647459
## iter 90 value 5.813813
## iter 100 value 4.556247
## final value 4.556247
## stopped after 100 iterations
## # weights: 179
## initial value 106.316868
## iter 10 value 23.770381
## iter 20 value 11.783803
## iter 30 value 9.676904
## iter 40 value 8.118734
## iter 50 value 7.545715
## iter 60 value 7.511020
## iter 70 value 7.445875
## iter 80 value 7.423390
## iter 90 value 7.414385
## iter 100 value 7.367584
## final value 7.367584
## stopped after 100 iterations
## # weights: 39
## initial value 105.344985
## iter 10 value 60.114813
## iter 20 value 52.056150
## iter 30 value 48.603365
## iter 40 value 47.673760
## iter 50 value 47.586850
## iter 60 value 47.504725
## iter 70 value 47.190616
## iter 80 value 46.238303
## iter 90 value 46.055363
## iter 100 value 45.661020
## final value 45.661020
## stopped after 100 iterations
## # weights: 109
## initial value 100.276078
## iter 10 value 43.295442
## iter 20 value 25.915349
## iter 30 value 23.194969
## iter 40 value 21.158871
## iter 50 value 19.071355
## iter 60 value 17.812854
## iter 70 value 16.256714
## iter 80 value 8.509209
## iter 90 value 6.254669
## iter 100 value 5.901522
## final value 5.901522
## stopped after 100 iterations
## # weights: 179
## initial value 97.761042
## iter 10 value 17.154700
## iter 20 value 7.829067
## iter 30 value 3.227378
## iter 40 value 2.747947
## iter 50 value 0.046893
## iter 60 value 0.015838
## iter 70 value 0.006520
## iter 80 value 0.003891
## iter 90 value 0.002845
## iter 100 value 0.001090
## final value 0.001090
## stopped after 100 iterations
## # weights: 39
## initial value 101.671479
## iter 10 value 70.382295
## iter 20 value 62.066615
## iter 30 value 59.764257
## iter 40 value 59.088796
## iter 50 value 58.916054
## iter 60 value 58.834693
## iter 70 value 58.832265
## final value 58.832239
## converged
## # weights: 109
## initial value 107.998837
## iter 10 value 60.476972
## iter 20 value 45.654734
## iter 30 value 31.672688
## iter 40 value 20.794969
## iter 50 value 18.011520
## iter 60 value 16.053407
## iter 70 value 15.741586
## iter 80 value 15.728957
## iter 90 value 15.728822
## final value 15.728820
## converged
## # weights: 179
## initial value 123.720915
## iter 10 value 40.468597
## iter 20 value 25.862052
## iter 30 value 17.573230
## iter 40 value 14.579384
## iter 50 value 13.731864
## iter 60 value 13.452821
## iter 70 value 12.803875
## iter 80 value 12.559815
## iter 90 value 12.364204
## iter 100 value 12.345274
## final value 12.345274
## stopped after 100 iterations
## # weights: 39
## initial value 104.100554
## iter 10 value 66.890695
## iter 20 value 56.156042
## iter 30 value 51.173877
## iter 40 value 46.797223
## iter 50 value 44.345010
## iter 60 value 44.239260
## iter 70 value 43.372208
## iter 80 value 43.302513
## iter 90 value 42.916780
## iter 100 value 42.882260
## final value 42.882260
## stopped after 100 iterations
## # weights: 109
## initial value 110.092708
## iter 10 value 44.928603
## iter 20 value 32.047071
## iter 30 value 28.337515
## iter 40 value 27.179590
## iter 50 value 26.412745
## iter 60 value 24.530238
## iter 70 value 24.468218
## iter 80 value 22.752311
## iter 90 value 21.922952
## iter 100 value 21.888506
## final value 21.888506
## stopped after 100 iterations
## # weights: 179
## initial value 117.480129
## iter 10 value 26.056083
## iter 20 value 14.870582
## iter 30 value 10.580739
## iter 40 value 10.144599
## iter 50 value 9.985516
## iter 60 value 9.963580
## iter 70 value 9.921854
## iter 80 value 9.786458
## iter 90 value 7.964102
## iter 100 value 6.558887
## final value 6.558887
## stopped after 100 iterations
## # weights: 39
## initial value 98.626120
## iter 10 value 79.910268
## iter 20 value 68.317892
## iter 30 value 65.890290
## iter 40 value 62.895747
## iter 50 value 59.481216
## iter 60 value 57.686905
## iter 70 value 57.410938
## iter 80 value 57.292126
## iter 90 value 57.276123
## iter 100 value 57.271071
## final value 57.271071
## stopped after 100 iterations
## # weights: 109
## initial value 144.941972
## iter 10 value 77.123106
## iter 20 value 24.284521
## iter 30 value 19.662510
## iter 40 value 18.675186
## iter 50 value 16.316879
## iter 60 value 16.023601
## iter 70 value 15.582439
## iter 80 value 15.484077
## iter 90 value 14.857626
## iter 100 value 14.839381
## final value 14.839381
## stopped after 100 iterations
## # weights: 179
## initial value 104.057813
## iter 10 value 15.322900
## iter 20 value 2.267731
## iter 30 value 1.440125
## iter 40 value 1.386743
## final value 1.386299
## converged
## # weights: 39
## initial value 103.571185
## iter 10 value 70.173304
## iter 20 value 63.246199
## iter 30 value 60.504459
## iter 40 value 60.162146
## iter 50 value 59.895116
## iter 60 value 59.401383
## iter 70 value 58.845643
## final value 58.844214
## converged
## # weights: 109
## initial value 114.942385
## iter 10 value 47.235792
## iter 20 value 31.410793
## iter 30 value 23.149571
## iter 40 value 18.383528
## iter 50 value 16.339475
## iter 60 value 16.044604
## iter 70 value 15.984868
## iter 80 value 15.739692
## iter 90 value 15.726611
## iter 100 value 15.726518
## final value 15.726518
## stopped after 100 iterations
## # weights: 179
## initial value 114.203579
## iter 10 value 30.198653
## iter 20 value 17.327694
## iter 30 value 13.825932
## iter 40 value 12.743544
## iter 50 value 12.296795
## iter 60 value 12.202201
## iter 70 value 12.167712
## iter 80 value 12.164789
## iter 90 value 12.164686
## final value 12.164678
## converged
## # weights: 39
## initial value 105.847642
## iter 10 value 81.982261
## iter 20 value 64.064822
## iter 30 value 63.955642
## iter 40 value 63.954360
## iter 50 value 63.948536
## iter 60 value 63.917226
## iter 70 value 59.457799
## iter 80 value 58.373931
## iter 90 value 58.037982
## iter 100 value 57.187626
## final value 57.187626
## stopped after 100 iterations
## # weights: 109
## initial value 106.936039
## iter 10 value 37.802223
## iter 20 value 19.992552
## iter 30 value 11.378917
## iter 40 value 10.923480
## iter 50 value 6.559897
## iter 60 value 5.622165
## iter 70 value 5.569550
## iter 80 value 5.500612
## iter 90 value 0.282542
## iter 100 value 0.240582
## final value 0.240582
## stopped after 100 iterations
## # weights: 179
## initial value 126.812508
## iter 10 value 19.841783
## iter 20 value 0.682379
## iter 30 value 0.171284
## iter 40 value 0.151431
## iter 50 value 0.141022
## iter 60 value 0.126759
## iter 70 value 0.120292
## iter 80 value 0.113210
## iter 90 value 0.103905
## iter 100 value 0.100037
## final value 0.100037
## stopped after 100 iterations
## # weights: 39
## initial value 102.331851
## iter 10 value 77.118741
## iter 20 value 75.645531
## iter 30 value 74.549658
## iter 40 value 73.721185
## iter 50 value 73.186026
## iter 60 value 71.937548
## iter 70 value 68.992252
## iter 80 value 66.079788
## iter 90 value 65.765124
## iter 100 value 65.747099
## final value 65.747099
## stopped after 100 iterations
## # weights: 109
## initial value 108.028265
## iter 10 value 49.317289
## iter 20 value 35.085843
## iter 30 value 27.572547
## iter 40 value 24.487502
## iter 50 value 19.450854
## iter 60 value 13.490658
## iter 70 value 12.082220
## iter 80 value 11.724555
## iter 90 value 11.597832
## iter 100 value 11.154063
## final value 11.154063
## stopped after 100 iterations
## # weights: 179
## initial value 104.751551
## iter 10 value 29.066701
## iter 20 value 7.801708
## iter 30 value 6.297922
## iter 40 value 6.172064
## iter 50 value 5.233742
## iter 60 value 4.792694
## iter 70 value 4.782133
## iter 80 value 4.781743
## iter 90 value 4.781042
## iter 100 value 4.780860
## final value 4.780860
## stopped after 100 iterations
## # weights: 39
## initial value 103.743076
## iter 10 value 76.126151
## iter 20 value 63.917704
## iter 30 value 60.665179
## iter 40 value 59.454249
## iter 50 value 57.937775
## iter 60 value 57.913853
## final value 57.913845
## converged
## # weights: 109
## initial value 104.302171
## iter 10 value 48.039656
## iter 20 value 31.112935
## iter 30 value 23.007944
## iter 40 value 17.853326
## iter 50 value 17.175604
## iter 60 value 17.133322
## iter 70 value 17.132918
## final value 17.132917
## converged
## # weights: 179
## initial value 127.097661
## iter 10 value 48.790091
## iter 20 value 24.244372
## iter 30 value 14.552219
## iter 40 value 12.402271
## iter 50 value 12.250747
## iter 60 value 12.235741
## iter 70 value 12.233540
## iter 80 value 12.233358
## final value 12.233354
## converged
## # weights: 39
## initial value 101.214908
## iter 10 value 57.856495
## iter 20 value 49.857730
## iter 30 value 45.087983
## iter 40 value 42.571795
## iter 50 value 41.809917
## iter 60 value 41.786536
## iter 70 value 41.766615
## iter 80 value 41.749286
## iter 90 value 41.726401
## iter 100 value 41.717333
## final value 41.717333
## stopped after 100 iterations
## # weights: 109
## initial value 114.460738
## iter 10 value 26.376054
## iter 20 value 14.035897
## iter 30 value 5.341266
## iter 40 value 4.259323
## iter 50 value 4.086854
## iter 60 value 1.732371
## iter 70 value 0.225418
## iter 80 value 0.194038
## iter 90 value 0.178908
## iter 100 value 0.167144
## final value 0.167144
## stopped after 100 iterations
## # weights: 179
## initial value 100.455778
## iter 10 value 19.326568
## iter 20 value 2.203497
## iter 30 value 2.046491
## iter 40 value 2.032757
## iter 50 value 1.496368
## iter 60 value 0.157461
## iter 70 value 0.113411
## iter 80 value 0.107761
## iter 90 value 0.104948
## iter 100 value 0.098663
## final value 0.098663
## stopped after 100 iterations
## # weights: 39
## initial value 112.864063
## iter 10 value 62.462087
## iter 20 value 55.510725
## iter 30 value 52.142977
## iter 40 value 50.505459
## iter 50 value 48.869881
## iter 60 value 48.618819
## iter 70 value 47.891296
## iter 80 value 46.751051
## iter 90 value 45.436124
## iter 100 value 45.029033
## final value 45.029033
## stopped after 100 iterations
## # weights: 109
## initial value 118.942175
## iter 10 value 29.365561
## iter 20 value 13.913647
## iter 30 value 11.398030
## iter 40 value 9.533676
## iter 50 value 9.114137
## iter 60 value 9.023861
## iter 70 value 8.885837
## iter 80 value 8.826752
## iter 90 value 8.589774
## iter 100 value 8.241541
## final value 8.241541
## stopped after 100 iterations
## # weights: 179
## initial value 114.418584
## iter 10 value 27.335317
## iter 20 value 1.596331
## iter 30 value 0.059301
## iter 40 value 0.003534
## iter 50 value 0.000353
## iter 60 value 0.000242
## final value 0.000083
## converged
## # weights: 39
## initial value 100.799018
## iter 10 value 69.652839
## iter 20 value 62.652819
## iter 30 value 60.119425
## iter 40 value 59.945466
## iter 50 value 59.475821
## iter 60 value 58.757867
## iter 70 value 58.432540
## iter 80 value 58.424767
## final value 58.424747
## converged
## # weights: 109
## initial value 103.169973
## iter 10 value 55.363697
## iter 20 value 28.392282
## iter 30 value 20.062653
## iter 40 value 18.759972
## iter 50 value 18.545062
## iter 60 value 18.255249
## iter 70 value 17.149313
## iter 80 value 16.855156
## iter 90 value 16.027823
## iter 100 value 15.929265
## final value 15.929265
## stopped after 100 iterations
## # weights: 179
## initial value 111.943346
## iter 10 value 49.714763
## iter 20 value 28.826984
## iter 30 value 16.145633
## iter 40 value 13.411749
## iter 50 value 12.947011
## iter 60 value 12.718059
## iter 70 value 12.537811
## iter 80 value 12.517755
## iter 90 value 12.516124
## iter 100 value 12.515708
## final value 12.515708
## stopped after 100 iterations
## # weights: 39
## initial value 103.759376
## iter 10 value 66.053519
## iter 20 value 57.723898
## iter 30 value 57.338882
## iter 40 value 56.280778
## iter 50 value 56.245932
## iter 60 value 56.226694
## iter 70 value 55.134037
## iter 80 value 53.871905
## iter 90 value 52.680799
## iter 100 value 52.632227
## final value 52.632227
## stopped after 100 iterations
## # weights: 109
## initial value 118.250672
## iter 10 value 33.272180
## iter 20 value 19.501960
## iter 30 value 14.308341
## iter 40 value 12.284254
## iter 50 value 10.582490
## iter 60 value 10.508805
## iter 70 value 10.421186
## iter 80 value 8.018689
## iter 90 value 7.806529
## iter 100 value 7.793830
## final value 7.793830
## stopped after 100 iterations
## # weights: 179
## initial value 148.202269
## iter 10 value 56.558341
## iter 20 value 26.479386
## iter 30 value 19.184836
## iter 40 value 16.191693
## iter 50 value 13.423162
## iter 60 value 12.744376
## iter 70 value 11.780772
## iter 80 value 9.320126
## iter 90 value 7.417660
## iter 100 value 5.583016
## final value 5.583016
## stopped after 100 iterations
## # weights: 39
## initial value 102.418690
## iter 10 value 71.055217
## iter 20 value 67.158590
## iter 30 value 66.769307
## iter 40 value 64.429576
## iter 50 value 58.258540
## iter 60 value 57.853237
## iter 70 value 56.987415
## iter 80 value 55.347471
## iter 90 value 54.955668
## iter 100 value 54.942286
## final value 54.942286
## stopped after 100 iterations
## # weights: 109
## initial value 107.352464
## iter 10 value 47.984754
## iter 20 value 33.645589
## iter 30 value 16.722743
## iter 40 value 14.057835
## iter 50 value 13.439227
## iter 60 value 12.480507
## iter 70 value 8.779317
## iter 80 value 8.001448
## iter 90 value 6.484014
## iter 100 value 5.279890
## final value 5.279890
## stopped after 100 iterations
## # weights: 179
## initial value 108.400527
## iter 10 value 29.485720
## iter 20 value 13.479316
## iter 30 value 6.086225
## iter 40 value 2.366901
## iter 50 value 1.943584
## iter 60 value 1.925196
## iter 70 value 1.918077
## iter 80 value 1.911607
## iter 90 value 1.910946
## iter 100 value 1.910353
## final value 1.910353
## stopped after 100 iterations
## # weights: 39
## initial value 100.498558
## iter 10 value 72.863891
## iter 20 value 65.038302
## iter 30 value 62.552814
## iter 40 value 59.521106
## iter 50 value 58.275421
## iter 60 value 57.626380
## iter 70 value 57.549974
## final value 57.549638
## converged
## # weights: 109
## initial value 116.104385
## iter 10 value 37.909599
## iter 20 value 26.356494
## iter 30 value 21.022665
## iter 40 value 17.201416
## iter 50 value 16.109427
## iter 60 value 16.071303
## final value 16.071091
## converged
## # weights: 179
## initial value 104.956466
## iter 10 value 36.318322
## iter 20 value 20.596644
## iter 30 value 14.937412
## iter 40 value 13.056861
## iter 50 value 12.407184
## iter 60 value 12.159840
## iter 70 value 11.921136
## iter 80 value 11.794267
## iter 90 value 11.777995
## iter 100 value 11.777649
## final value 11.777649
## stopped after 100 iterations
## # weights: 39
## initial value 101.454706
## iter 10 value 83.182233
## iter 20 value 65.771691
## iter 30 value 65.682662
## iter 40 value 64.496917
## iter 50 value 63.703936
## iter 60 value 63.678678
## iter 70 value 63.670216
## iter 80 value 63.664699
## iter 90 value 63.660485
## iter 100 value 63.282982
## final value 63.282982
## stopped after 100 iterations
## # weights: 109
## initial value 107.794895
## iter 10 value 39.211013
## iter 20 value 17.750283
## iter 30 value 14.292229
## iter 40 value 13.848316
## iter 50 value 13.789629
## iter 60 value 13.739810
## iter 70 value 13.630570
## iter 80 value 13.455004
## iter 90 value 11.003166
## iter 100 value 7.783344
## final value 7.783344
## stopped after 100 iterations
## # weights: 179
## initial value 103.415146
## iter 10 value 24.953071
## iter 20 value 6.832968
## iter 30 value 4.254160
## iter 40 value 3.998683
## iter 50 value 3.900428
## iter 60 value 3.819728
## iter 70 value 3.668319
## iter 80 value 3.467882
## iter 90 value 3.215982
## iter 100 value 3.206754
## final value 3.206754
## stopped after 100 iterations
## # weights: 39
## initial value 103.877719
## iter 10 value 64.147785
## iter 20 value 51.995981
## iter 30 value 51.241590
## iter 40 value 51.212248
## iter 50 value 51.176162
## iter 60 value 50.635683
## iter 70 value 50.487206
## final value 50.487088
## converged
## # weights: 109
## initial value 104.283856
## iter 10 value 32.515271
## iter 20 value 12.151861
## iter 30 value 9.025931
## iter 40 value 8.576796
## iter 50 value 8.055914
## iter 60 value 7.802894
## iter 70 value 7.287348
## iter 80 value 7.229937
## iter 90 value 7.218107
## iter 100 value 7.209943
## final value 7.209943
## stopped after 100 iterations
## # weights: 179
## initial value 144.903137
## iter 10 value 44.076925
## iter 20 value 17.596644
## iter 30 value 15.087289
## iter 40 value 14.563269
## iter 50 value 10.352749
## iter 60 value 6.475133
## iter 70 value 6.282097
## iter 80 value 5.545461
## iter 90 value 5.545303
## iter 100 value 5.545264
## final value 5.545264
## stopped after 100 iterations
## # weights: 39
## initial value 101.783259
## iter 10 value 83.271780
## iter 20 value 63.992951
## iter 30 value 58.957729
## iter 40 value 58.767410
## iter 50 value 58.764643
## final value 58.764475
## converged
## # weights: 109
## initial value 100.139730
## iter 10 value 43.931145
## iter 20 value 23.671386
## iter 30 value 17.530501
## iter 40 value 16.050389
## iter 50 value 15.651794
## iter 60 value 15.617358
## final value 15.617284
## converged
## # weights: 179
## initial value 102.106877
## iter 10 value 34.941892
## iter 20 value 17.666120
## iter 30 value 13.932899
## iter 40 value 13.055006
## iter 50 value 12.705135
## iter 60 value 12.620219
## iter 70 value 12.591342
## iter 80 value 12.582013
## iter 90 value 12.581567
## final value 12.581563
## converged
## # weights: 39
## initial value 103.317784
## iter 10 value 76.721712
## iter 20 value 61.428351
## iter 30 value 58.273222
## iter 40 value 56.857703
## iter 50 value 54.590488
## iter 60 value 53.882255
## iter 70 value 53.662346
## iter 80 value 53.332685
## iter 90 value 52.775318
## iter 100 value 52.720022
## final value 52.720022
## stopped after 100 iterations
## # weights: 109
## initial value 129.941677
## iter 10 value 60.195476
## iter 20 value 27.532384
## iter 30 value 10.376122
## iter 40 value 5.788703
## iter 50 value 4.396850
## iter 60 value 3.885895
## iter 70 value 3.761280
## iter 80 value 3.742005
## iter 90 value 3.725927
## iter 100 value 3.687669
## final value 3.687669
## stopped after 100 iterations
## # weights: 179
## initial value 111.463558
## iter 10 value 18.731761
## iter 20 value 7.272737
## iter 30 value 4.765955
## iter 40 value 3.170226
## iter 50 value 2.994089
## iter 60 value 2.974798
## iter 70 value 2.967666
## iter 80 value 1.626395
## iter 90 value 1.569963
## iter 100 value 0.207197
## final value 0.207197
## stopped after 100 iterations
## # weights: 39
## initial value 100.126293
## iter 10 value 68.405075
## iter 20 value 55.765061
## iter 30 value 55.032503
## iter 40 value 51.629556
## iter 50 value 47.032136
## iter 60 value 42.566856
## iter 70 value 41.546683
## iter 80 value 41.468771
## iter 90 value 41.434893
## iter 100 value 41.252546
## final value 41.252546
## stopped after 100 iterations
## # weights: 109
## initial value 106.303020
## iter 10 value 48.377013
## iter 20 value 22.074398
## iter 30 value 17.603824
## iter 40 value 17.158395
## iter 50 value 17.116130
## iter 60 value 17.102090
## iter 70 value 17.092924
## iter 80 value 17.088672
## iter 90 value 16.891493
## iter 100 value 16.867100
## final value 16.867100
## stopped after 100 iterations
## # weights: 179
## initial value 105.622444
## iter 10 value 22.276828
## iter 20 value 11.035662
## iter 30 value 7.575096
## iter 40 value 7.254733
## iter 50 value 7.188528
## iter 60 value 6.837464
## iter 70 value 5.349598
## iter 80 value 4.492616
## iter 90 value 3.960821
## iter 100 value 1.497602
## final value 1.497602
## stopped after 100 iterations
## # weights: 39
## initial value 103.204839
## iter 10 value 70.888866
## iter 20 value 64.854832
## iter 30 value 60.505710
## iter 40 value 59.323505
## iter 50 value 59.110164
## iter 60 value 59.051548
## iter 70 value 59.035015
## iter 70 value 59.035015
## iter 70 value 59.035015
## final value 59.035015
## converged
## # weights: 109
## initial value 128.849614
## iter 10 value 61.938800
## iter 20 value 36.552475
## iter 30 value 22.405648
## iter 40 value 17.283416
## iter 50 value 16.586665
## iter 60 value 16.515742
## iter 70 value 16.477041
## iter 80 value 16.466998
## iter 90 value 16.466202
## final value 16.466183
## converged
## # weights: 179
## initial value 110.271610
## iter 10 value 50.180700
## iter 20 value 31.554709
## iter 30 value 17.315662
## iter 40 value 12.944208
## iter 50 value 12.251342
## iter 60 value 12.159385
## iter 70 value 12.150538
## iter 80 value 12.149933
## iter 90 value 12.149916
## final value 12.149915
## converged
## # weights: 39
## initial value 116.089303
## iter 10 value 67.748410
## iter 20 value 61.994831
## iter 30 value 59.615635
## iter 40 value 58.915516
## iter 50 value 58.520884
## iter 60 value 58.472508
## iter 70 value 58.370575
## iter 80 value 58.324050
## iter 90 value 58.128299
## iter 100 value 57.037993
## final value 57.037993
## stopped after 100 iterations
## # weights: 109
## initial value 115.247747
## iter 10 value 54.195659
## iter 20 value 26.147525
## iter 30 value 11.149365
## iter 40 value 7.042627
## iter 50 value 5.315308
## iter 60 value 5.088945
## iter 70 value 4.482849
## iter 80 value 3.926541
## iter 90 value 2.850173
## iter 100 value 0.233829
## final value 0.233829
## stopped after 100 iterations
## # weights: 179
## initial value 120.202035
## iter 10 value 24.849869
## iter 20 value 5.699435
## iter 30 value 2.140725
## iter 40 value 0.298732
## iter 50 value 0.233885
## iter 60 value 0.218877
## iter 70 value 0.201590
## iter 80 value 0.185795
## iter 90 value 0.166678
## iter 100 value 0.154118
## final value 0.154118
## stopped after 100 iterations
## # weights: 39
## initial value 111.245106
## iter 10 value 66.224925
## iter 20 value 58.223940
## iter 30 value 56.860679
## iter 40 value 56.397640
## iter 50 value 55.970450
## iter 60 value 55.966223
## iter 70 value 55.957568
## iter 80 value 55.952434
## iter 90 value 55.949369
## iter 100 value 54.456930
## final value 54.456930
## stopped after 100 iterations
## # weights: 109
## initial value 123.392432
## iter 10 value 45.416071
## iter 20 value 36.055904
## iter 30 value 31.993854
## iter 40 value 30.507479
## iter 50 value 29.519126
## iter 60 value 28.094557
## iter 70 value 25.689433
## iter 80 value 25.554975
## iter 90 value 24.800984
## iter 100 value 23.651460
## final value 23.651460
## stopped after 100 iterations
## # weights: 179
## initial value 105.709171
## iter 10 value 43.103155
## iter 20 value 14.170640
## iter 30 value 12.900424
## iter 40 value 12.833486
## iter 50 value 12.823268
## iter 60 value 12.821913
## final value 12.821723
## converged
## # weights: 39
## initial value 105.265412
## iter 10 value 81.937971
## iter 20 value 70.282207
## iter 30 value 62.482980
## iter 40 value 59.963098
## iter 50 value 59.667168
## final value 59.663345
## converged
## # weights: 109
## initial value 105.580125
## iter 10 value 63.714006
## iter 20 value 35.025028
## iter 30 value 21.801435
## iter 40 value 17.655637
## iter 50 value 16.452754
## iter 60 value 16.089910
## iter 70 value 15.963306
## iter 80 value 15.961979
## final value 15.961975
## converged
## # weights: 179
## initial value 114.650738
## iter 10 value 41.052686
## iter 20 value 21.329416
## iter 30 value 14.684476
## iter 40 value 13.090797
## iter 50 value 12.767833
## iter 60 value 12.689669
## iter 70 value 12.600563
## iter 80 value 12.503758
## iter 90 value 12.409280
## iter 100 value 12.210580
## final value 12.210580
## stopped after 100 iterations
## # weights: 39
## initial value 115.811519
## iter 10 value 79.337877
## iter 20 value 58.080930
## iter 30 value 57.255951
## iter 40 value 57.123048
## iter 50 value 56.103605
## iter 60 value 55.394270
## iter 70 value 53.976200
## iter 80 value 53.766214
## iter 90 value 53.248921
## iter 100 value 52.902043
## final value 52.902043
## stopped after 100 iterations
## # weights: 109
## initial value 117.923969
## iter 10 value 24.646291
## iter 20 value 14.026214
## iter 30 value 9.177271
## iter 40 value 5.924326
## iter 50 value 5.483767
## iter 60 value 5.135268
## iter 70 value 4.958622
## iter 80 value 4.928019
## iter 90 value 4.916795
## iter 100 value 4.661728
## final value 4.661728
## stopped after 100 iterations
## # weights: 179
## initial value 116.128278
## iter 10 value 42.986851
## iter 20 value 14.446667
## iter 30 value 11.459803
## iter 40 value 8.716805
## iter 50 value 8.694895
## iter 60 value 8.657438
## iter 70 value 8.621765
## iter 80 value 8.574368
## iter 90 value 8.551392
## iter 100 value 8.529179
## final value 8.529179
## stopped after 100 iterations
## # weights: 39
## initial value 107.340911
## iter 10 value 72.660866
## iter 20 value 66.396492
## iter 30 value 65.011520
## iter 40 value 64.354710
## iter 50 value 64.190403
## iter 60 value 64.042797
## iter 70 value 62.329625
## iter 80 value 62.147423
## iter 90 value 62.069756
## iter 100 value 61.801182
## final value 61.801182
## stopped after 100 iterations
## # weights: 109
## initial value 103.874866
## iter 10 value 23.723727
## iter 20 value 18.175229
## iter 30 value 16.711179
## iter 40 value 16.525947
## iter 50 value 15.656980
## iter 60 value 15.653166
## iter 70 value 15.652339
## iter 80 value 11.993317
## iter 90 value 11.857439
## iter 100 value 11.846196
## final value 11.846196
## stopped after 100 iterations
## # weights: 179
## initial value 113.038569
## iter 10 value 32.378972
## iter 20 value 8.421653
## iter 30 value 4.424333
## iter 40 value 3.355625
## iter 50 value 3.324333
## iter 60 value 3.139607
## iter 70 value 3.055359
## final value 3.014211
## converged
## # weights: 39
## initial value 99.041836
## iter 10 value 67.109224
## iter 20 value 60.501539
## iter 30 value 59.480042
## iter 40 value 59.323734
## iter 50 value 59.118990
## iter 60 value 59.041670
## iter 70 value 59.004644
## iter 80 value 59.000747
## iter 90 value 59.000643
## iter 90 value 59.000642
## iter 90 value 59.000642
## final value 59.000642
## converged
## # weights: 109
## initial value 101.656206
## iter 10 value 54.337202
## iter 20 value 35.092868
## iter 30 value 20.243122
## iter 40 value 17.059275
## iter 50 value 16.739259
## iter 60 value 16.728669
## iter 70 value 16.728506
## final value 16.728505
## converged
## # weights: 179
## initial value 123.961804
## iter 10 value 43.347079
## iter 20 value 23.015554
## iter 30 value 15.211703
## iter 40 value 13.614319
## iter 50 value 13.066238
## iter 60 value 12.610179
## iter 70 value 12.497111
## iter 80 value 12.479448
## iter 90 value 12.479088
## final value 12.479084
## converged
## # weights: 39
## initial value 108.017094
## iter 10 value 65.099989
## iter 20 value 59.685023
## iter 30 value 57.108309
## iter 40 value 56.280465
## iter 50 value 55.785627
## iter 60 value 55.453078
## iter 70 value 55.036672
## iter 80 value 53.334857
## iter 90 value 52.551244
## iter 100 value 52.147786
## final value 52.147786
## stopped after 100 iterations
## # weights: 109
## initial value 116.672782
## iter 10 value 41.651438
## iter 20 value 27.238467
## iter 30 value 23.052093
## iter 40 value 19.038733
## iter 50 value 15.491976
## iter 60 value 11.624136
## iter 70 value 10.343665
## iter 80 value 9.972205
## iter 90 value 9.149049
## iter 100 value 8.944735
## final value 8.944735
## stopped after 100 iterations
## # weights: 179
## initial value 94.282572
## iter 10 value 22.961099
## iter 20 value 7.777698
## iter 30 value 0.681871
## iter 40 value 0.224789
## iter 50 value 0.192345
## iter 60 value 0.176125
## iter 70 value 0.165881
## iter 80 value 0.152971
## iter 90 value 0.143239
## iter 100 value 0.134904
## final value 0.134904
## stopped after 100 iterations
## # weights: 39
## initial value 110.021643
## iter 10 value 68.212301
## iter 20 value 59.550413
## iter 30 value 59.481303
## iter 40 value 59.392364
## iter 50 value 59.143030
## iter 60 value 57.941421
## iter 70 value 57.452159
## iter 80 value 57.352470
## iter 90 value 57.315200
## iter 100 value 51.377015
## final value 51.377015
## stopped after 100 iterations
## # weights: 109
## initial value 102.449494
## iter 10 value 44.123670
## iter 20 value 39.494610
## iter 30 value 36.662274
## iter 40 value 36.340838
## iter 50 value 36.286334
## iter 60 value 28.772854
## iter 70 value 26.174654
## iter 80 value 23.531557
## iter 90 value 22.911295
## iter 100 value 22.525275
## final value 22.525275
## stopped after 100 iterations
## # weights: 179
## initial value 109.368167
## iter 10 value 32.193699
## iter 20 value 7.439406
## iter 30 value 6.206705
## iter 40 value 6.078528
## iter 50 value 5.561726
## iter 60 value 5.463691
## iter 70 value 5.024068
## iter 80 value 5.023187
## iter 90 value 5.022409
## iter 100 value 5.022270
## final value 5.022270
## stopped after 100 iterations
## # weights: 39
## initial value 109.439745
## iter 10 value 73.986853
## iter 20 value 63.028384
## iter 30 value 62.211043
## iter 40 value 60.641053
## iter 50 value 58.404440
## iter 60 value 58.245921
## final value 58.245131
## converged
## # weights: 109
## initial value 107.880325
## iter 10 value 53.370218
## iter 20 value 26.541850
## iter 30 value 18.213279
## iter 40 value 16.622744
## iter 50 value 15.745120
## iter 60 value 15.589815
## iter 70 value 15.571255
## iter 80 value 15.570163
## final value 15.570148
## converged
## # weights: 179
## initial value 111.497857
## iter 10 value 43.211401
## iter 20 value 19.413065
## iter 30 value 13.084488
## iter 40 value 12.228906
## iter 50 value 11.910745
## iter 60 value 11.877340
## iter 70 value 11.811154
## iter 80 value 11.695662
## iter 90 value 11.690528
## iter 100 value 11.690448
## final value 11.690448
## stopped after 100 iterations
## # weights: 39
## initial value 103.125773
## iter 10 value 71.451501
## iter 20 value 61.829624
## iter 30 value 56.122091
## iter 40 value 53.219752
## iter 50 value 52.470908
## iter 60 value 52.453926
## iter 70 value 52.418181
## iter 80 value 52.376890
## iter 90 value 52.374963
## iter 100 value 52.367397
## final value 52.367397
## stopped after 100 iterations
## # weights: 109
## initial value 99.332307
## iter 10 value 37.815508
## iter 20 value 21.659869
## iter 30 value 14.800785
## iter 40 value 10.486945
## iter 50 value 9.264006
## iter 60 value 8.688543
## iter 70 value 8.076080
## iter 80 value 7.965414
## iter 90 value 7.813975
## iter 100 value 7.733311
## final value 7.733311
## stopped after 100 iterations
## # weights: 179
## initial value 121.814606
## iter 10 value 38.978621
## iter 20 value 13.983112
## iter 30 value 6.622314
## iter 40 value 4.770951
## iter 50 value 3.858405
## iter 60 value 3.551938
## iter 70 value 3.525053
## iter 80 value 3.497984
## iter 90 value 3.443982
## iter 100 value 3.393790
## final value 3.393790
## stopped after 100 iterations
## # weights: 39
## initial value 102.174818
## iter 10 value 67.613526
## iter 20 value 59.602468
## iter 30 value 56.104884
## iter 40 value 52.593574
## iter 50 value 50.422135
## iter 60 value 48.705386
## iter 70 value 46.473855
## iter 80 value 45.935973
## iter 90 value 44.882313
## iter 100 value 42.778879
## final value 42.778879
## stopped after 100 iterations
## # weights: 109
## initial value 123.849203
## iter 10 value 44.407363
## iter 20 value 27.265906
## iter 30 value 25.611426
## iter 40 value 24.241634
## iter 50 value 23.730479
## iter 60 value 23.511143
## iter 70 value 23.332120
## iter 80 value 23.039623
## iter 90 value 23.029611
## iter 100 value 23.028798
## final value 23.028798
## stopped after 100 iterations
## # weights: 179
## initial value 102.813888
## iter 10 value 10.676891
## iter 20 value 3.874461
## iter 30 value 0.197353
## iter 40 value 0.022392
## iter 50 value 0.006858
## iter 60 value 0.003255
## iter 70 value 0.002284
## iter 80 value 0.000879
## iter 90 value 0.000760
## iter 100 value 0.000644
## final value 0.000644
## stopped after 100 iterations
## # weights: 39
## initial value 114.315697
## iter 10 value 76.631101
## iter 20 value 63.977421
## iter 30 value 60.801777
## iter 40 value 58.862362
## iter 50 value 58.805680
## final value 58.805662
## converged
## # weights: 109
## initial value 106.364714
## iter 10 value 51.340241
## iter 20 value 31.023406
## iter 30 value 23.868594
## iter 40 value 21.237593
## iter 50 value 17.254159
## iter 60 value 15.957460
## iter 70 value 15.783566
## iter 80 value 15.782541
## final value 15.782540
## converged
## # weights: 179
## initial value 118.194570
## iter 10 value 49.684520
## iter 20 value 26.817583
## iter 30 value 18.452851
## iter 40 value 16.126790
## iter 50 value 15.198806
## iter 60 value 13.817211
## iter 70 value 12.730359
## iter 80 value 12.569357
## iter 90 value 12.555857
## iter 100 value 12.555210
## final value 12.555210
## stopped after 100 iterations
## # weights: 39
## initial value 104.093704
## iter 10 value 87.064566
## iter 20 value 73.034495
## iter 30 value 64.654842
## iter 40 value 62.123315
## iter 50 value 60.579212
## iter 60 value 56.954798
## iter 70 value 56.276110
## iter 80 value 56.235138
## iter 90 value 56.114725
## iter 100 value 56.111023
## final value 56.111023
## stopped after 100 iterations
## # weights: 109
## initial value 105.599813
## iter 10 value 33.900488
## iter 20 value 20.542418
## iter 30 value 16.328696
## iter 40 value 12.349470
## iter 50 value 11.675740
## iter 60 value 11.176623
## iter 70 value 10.955960
## iter 80 value 10.919433
## iter 90 value 10.340963
## iter 100 value 10.194449
## final value 10.194449
## stopped after 100 iterations
## # weights: 179
## initial value 121.186413
## iter 10 value 30.571532
## iter 20 value 10.703225
## iter 30 value 6.003909
## iter 40 value 4.975763
## iter 50 value 4.935455
## iter 60 value 4.291098
## iter 70 value 2.088678
## iter 80 value 2.056861
## iter 90 value 0.178236
## iter 100 value 0.148225
## final value 0.148225
## stopped after 100 iterations
## # weights: 39
## initial value 110.877185
## iter 10 value 76.407135
## iter 20 value 72.348556
## iter 30 value 69.966259
## iter 40 value 69.547092
## iter 50 value 69.498840
## iter 60 value 69.495193
## iter 70 value 69.493092
## iter 80 value 69.492085
## iter 90 value 69.491704
## iter 100 value 69.491507
## final value 69.491507
## stopped after 100 iterations
## # weights: 109
## initial value 105.196367
## iter 10 value 37.023872
## iter 20 value 17.167431
## iter 30 value 9.115901
## iter 40 value 8.242085
## iter 50 value 7.944407
## iter 60 value 7.800878
## iter 70 value 7.691244
## iter 80 value 7.599190
## iter 90 value 7.447350
## iter 100 value 6.996422
## final value 6.996422
## stopped after 100 iterations
## # weights: 179
## initial value 106.516563
## iter 10 value 20.375429
## iter 20 value 5.266175
## iter 30 value 3.049331
## iter 40 value 0.101928
## iter 50 value 0.018554
## iter 60 value 0.006918
## iter 70 value 0.001465
## iter 80 value 0.000610
## iter 90 value 0.000306
## iter 100 value 0.000224
## final value 0.000224
## stopped after 100 iterations
## # weights: 39
## initial value 101.404151
## iter 10 value 77.673161
## iter 20 value 64.204767
## iter 30 value 60.219105
## iter 40 value 58.820615
## iter 50 value 56.904625
## iter 60 value 56.388151
## iter 70 value 56.328674
## final value 56.328205
## converged
## # weights: 109
## initial value 101.292210
## iter 10 value 50.777953
## iter 20 value 29.976999
## iter 30 value 22.164987
## iter 40 value 17.258583
## iter 50 value 17.016999
## iter 60 value 16.955515
## iter 70 value 16.954735
## iter 70 value 16.954735
## iter 70 value 16.954735
## final value 16.954735
## converged
## # weights: 179
## initial value 104.055174
## iter 10 value 47.348492
## iter 20 value 23.581423
## iter 30 value 15.088989
## iter 40 value 13.470877
## iter 50 value 12.780743
## iter 60 value 12.646527
## iter 70 value 12.619079
## iter 80 value 12.615867
## iter 90 value 12.615342
## final value 12.615286
## converged
## # weights: 39
## initial value 113.953935
## iter 10 value 74.348849
## iter 20 value 64.698372
## iter 30 value 60.667436
## iter 40 value 55.076762
## iter 50 value 54.340443
## iter 60 value 53.771952
## iter 70 value 52.066827
## iter 80 value 51.695259
## iter 90 value 49.969988
## iter 100 value 49.864780
## final value 49.864780
## stopped after 100 iterations
## # weights: 109
## initial value 105.875971
## iter 10 value 30.724684
## iter 20 value 20.858453
## iter 30 value 20.198774
## iter 40 value 20.147281
## iter 50 value 20.127714
## iter 60 value 19.977423
## iter 70 value 18.690555
## iter 80 value 18.638091
## iter 90 value 18.600364
## iter 100 value 15.358080
## final value 15.358080
## stopped after 100 iterations
## # weights: 179
## initial value 113.916567
## iter 10 value 22.879010
## iter 20 value 19.433213
## iter 30 value 16.111486
## iter 40 value 14.549640
## iter 50 value 14.292227
## iter 60 value 14.034004
## iter 70 value 13.700443
## iter 80 value 13.235370
## iter 90 value 13.043564
## iter 100 value 12.931508
## final value 12.931508
## stopped after 100 iterations
## # weights: 39
## initial value 101.336606
## iter 10 value 58.713996
## iter 20 value 53.202679
## iter 30 value 50.970440
## iter 40 value 46.339869
## iter 50 value 45.662357
## iter 60 value 45.654508
## iter 70 value 45.654307
## iter 80 value 45.654286
## iter 80 value 45.654286
## iter 80 value 45.654286
## final value 45.654286
## converged
## # weights: 109
## initial value 113.403331
## iter 10 value 34.088838
## iter 20 value 16.824174
## iter 30 value 9.719780
## iter 40 value 7.304369
## iter 50 value 4.207232
## iter 60 value 3.889981
## iter 70 value 3.739552
## iter 80 value 3.509259
## iter 90 value 3.378364
## iter 100 value 3.346991
## final value 3.346991
## stopped after 100 iterations
## # weights: 179
## initial value 102.553090
## iter 10 value 30.479127
## iter 20 value 20.209764
## iter 30 value 14.850936
## iter 40 value 12.200090
## iter 50 value 9.978704
## iter 60 value 9.364917
## iter 70 value 9.270626
## iter 80 value 9.179696
## iter 90 value 8.911495
## iter 100 value 8.835730
## final value 8.835730
## stopped after 100 iterations
## # weights: 39
## initial value 123.515370
## iter 10 value 83.153644
## iter 20 value 70.419075
## iter 30 value 64.038411
## iter 40 value 62.307588
## iter 50 value 59.468386
## iter 60 value 58.712169
## iter 70 value 58.678013
## final value 58.677908
## converged
## # weights: 109
## initial value 109.985406
## iter 10 value 51.749223
## iter 20 value 35.665648
## iter 30 value 25.664766
## iter 40 value 21.912126
## iter 50 value 19.258182
## iter 60 value 17.923731
## iter 70 value 17.399123
## iter 80 value 17.054677
## iter 90 value 17.008316
## iter 100 value 16.999392
## final value 16.999392
## stopped after 100 iterations
## # weights: 179
## initial value 109.986602
## iter 10 value 40.930350
## iter 20 value 23.371774
## iter 30 value 15.478120
## iter 40 value 12.919731
## iter 50 value 12.301632
## iter 60 value 12.187109
## iter 70 value 12.177108
## iter 80 value 12.174443
## iter 90 value 12.174280
## final value 12.174272
## converged
## # weights: 39
## initial value 106.896869
## iter 10 value 75.647908
## iter 20 value 62.928572
## iter 30 value 60.338873
## iter 40 value 56.850236
## iter 50 value 54.587161
## iter 60 value 52.077134
## iter 70 value 49.266493
## iter 80 value 46.856633
## iter 90 value 46.431459
## iter 100 value 46.049128
## final value 46.049128
## stopped after 100 iterations
## # weights: 109
## initial value 104.227153
## iter 10 value 49.408174
## iter 20 value 33.398251
## iter 30 value 26.351524
## iter 40 value 21.805630
## iter 50 value 20.833078
## iter 60 value 20.257052
## iter 70 value 20.185209
## iter 80 value 20.136020
## iter 90 value 20.069878
## iter 100 value 20.020672
## final value 20.020672
## stopped after 100 iterations
## # weights: 179
## initial value 132.382320
## iter 10 value 67.797836
## iter 20 value 45.232275
## iter 30 value 22.550660
## iter 40 value 11.525468
## iter 50 value 9.119962
## iter 60 value 9.022706
## iter 70 value 8.996181
## iter 80 value 8.961726
## iter 90 value 8.433787
## iter 100 value 8.333212
## final value 8.333212
## stopped after 100 iterations
## # weights: 39
## initial value 106.255009
## iter 10 value 66.829748
## iter 20 value 61.989661
## iter 30 value 60.600386
## iter 40 value 59.364435
## iter 50 value 58.857610
## iter 60 value 58.583840
## iter 70 value 56.685739
## iter 80 value 55.652402
## iter 90 value 54.301840
## iter 100 value 54.060996
## final value 54.060996
## stopped after 100 iterations
## # weights: 109
## initial value 95.237205
## iter 10 value 46.263256
## iter 20 value 14.889634
## iter 30 value 9.748428
## iter 40 value 7.481983
## iter 50 value 6.447576
## iter 60 value 6.260024
## iter 70 value 6.245994
## iter 80 value 6.245665
## iter 90 value 6.243223
## iter 100 value 6.243155
## final value 6.243155
## stopped after 100 iterations
## # weights: 179
## initial value 103.675430
## iter 10 value 29.037170
## iter 20 value 10.186919
## iter 30 value 5.119725
## iter 40 value 2.403088
## iter 50 value 0.084056
## iter 60 value 0.012795
## iter 70 value 0.001609
## iter 80 value 0.000367
## iter 90 value 0.000108
## iter 90 value 0.000095
## iter 90 value 0.000095
## final value 0.000095
## converged
## # weights: 39
## initial value 101.282811
## iter 10 value 68.911659
## iter 20 value 65.579028
## iter 30 value 63.183165
## iter 40 value 60.489143
## iter 50 value 58.956900
## iter 60 value 58.877260
## iter 70 value 58.853650
## iter 80 value 58.814921
## iter 90 value 58.812273
## final value 58.812269
## converged
## # weights: 109
## initial value 115.216886
## iter 10 value 50.025580
## iter 20 value 28.517549
## iter 30 value 20.338125
## iter 40 value 16.609221
## iter 50 value 16.169486
## iter 60 value 16.134992
## iter 70 value 16.134684
## final value 16.134684
## converged
## # weights: 179
## initial value 116.085321
## iter 10 value 30.954048
## iter 20 value 18.705041
## iter 30 value 15.362583
## iter 40 value 13.987375
## iter 50 value 12.235154
## iter 60 value 11.987891
## iter 70 value 11.935045
## iter 80 value 11.889550
## iter 90 value 11.814101
## iter 100 value 11.698297
## final value 11.698297
## stopped after 100 iterations
## # weights: 39
## initial value 110.172818
## iter 10 value 66.795218
## iter 20 value 56.627134
## iter 30 value 55.113840
## iter 40 value 53.916655
## iter 50 value 53.513457
## iter 60 value 53.429708
## iter 70 value 53.424925
## iter 80 value 53.409361
## iter 90 value 53.323997
## iter 100 value 52.024818
## final value 52.024818
## stopped after 100 iterations
## # weights: 109
## initial value 105.943214
## iter 10 value 61.305445
## iter 20 value 46.566526
## iter 30 value 45.255248
## iter 40 value 44.839251
## iter 50 value 44.365836
## iter 60 value 43.802310
## iter 70 value 42.896344
## iter 80 value 42.581652
## iter 90 value 41.774748
## iter 100 value 41.655646
## final value 41.655646
## stopped after 100 iterations
## # weights: 179
## initial value 114.208948
## iter 10 value 13.133927
## iter 20 value 0.377683
## iter 30 value 0.206190
## iter 40 value 0.182578
## iter 50 value 0.161110
## iter 60 value 0.137105
## iter 70 value 0.126345
## iter 80 value 0.118637
## iter 90 value 0.111652
## iter 100 value 0.104741
## final value 0.104741
## stopped after 100 iterations
## # weights: 39
## initial value 97.141722
## iter 10 value 63.657723
## iter 20 value 56.257900
## iter 30 value 50.666105
## iter 40 value 43.475527
## iter 50 value 39.802194
## iter 60 value 38.635474
## iter 70 value 38.446019
## iter 80 value 38.401404
## iter 90 value 38.375354
## iter 100 value 38.361117
## final value 38.361117
## stopped after 100 iterations
## # weights: 109
## initial value 109.996305
## iter 10 value 22.052461
## iter 20 value 17.123695
## iter 30 value 16.218858
## iter 40 value 15.686876
## iter 50 value 14.917484
## iter 60 value 13.971951
## iter 70 value 12.201914
## iter 80 value 10.281824
## iter 90 value 10.224822
## iter 100 value 9.675752
## final value 9.675752
## stopped after 100 iterations
## # weights: 179
## initial value 130.458739
## iter 10 value 47.501540
## iter 20 value 23.194329
## iter 30 value 17.354286
## iter 40 value 16.617792
## iter 50 value 14.678477
## iter 60 value 14.372833
## iter 70 value 14.274735
## iter 80 value 14.227572
## iter 90 value 14.076666
## iter 100 value 13.723333
## final value 13.723333
## stopped after 100 iterations
## # weights: 39
## initial value 108.177681
## iter 10 value 72.116776
## iter 20 value 64.391265
## iter 30 value 60.224527
## iter 40 value 57.103019
## iter 50 value 55.732328
## iter 60 value 55.658225
## final value 55.657593
## converged
## # weights: 109
## initial value 106.246967
## iter 10 value 40.889581
## iter 20 value 27.603289
## iter 30 value 17.852300
## iter 40 value 16.264181
## iter 50 value 16.007933
## iter 60 value 15.766623
## iter 70 value 15.270262
## iter 80 value 14.501568
## iter 90 value 14.486939
## final value 14.486893
## converged
## # weights: 179
## initial value 100.280449
## iter 10 value 44.011581
## iter 20 value 22.418312
## iter 30 value 15.536459
## iter 40 value 13.070783
## iter 50 value 12.258427
## iter 60 value 11.768666
## iter 70 value 11.601077
## iter 80 value 11.450492
## iter 90 value 11.432543
## iter 100 value 11.409719
## final value 11.409719
## stopped after 100 iterations
## # weights: 39
## initial value 100.902533
## iter 10 value 69.495457
## iter 20 value 56.489412
## iter 30 value 52.155389
## iter 40 value 49.536678
## iter 50 value 47.961094
## iter 60 value 46.255032
## iter 70 value 45.697401
## iter 80 value 45.109495
## iter 90 value 44.840348
## iter 100 value 43.633264
## final value 43.633264
## stopped after 100 iterations
## # weights: 109
## initial value 105.241944
## iter 10 value 18.004443
## iter 20 value 13.521945
## iter 30 value 11.639554
## iter 40 value 11.310311
## iter 50 value 11.252484
## iter 60 value 11.203879
## iter 70 value 11.133833
## iter 80 value 11.101025
## iter 90 value 11.046981
## iter 100 value 11.000635
## final value 11.000635
## stopped after 100 iterations
## # weights: 179
## initial value 97.749766
## iter 10 value 25.020107
## iter 20 value 10.201067
## iter 30 value 8.053348
## iter 40 value 7.416027
## iter 50 value 6.988303
## iter 60 value 2.676693
## iter 70 value 2.448800
## iter 80 value 0.245567
## iter 90 value 0.207879
## iter 100 value 0.195222
## final value 0.195222
## stopped after 100 iterations
## # weights: 39
## initial value 102.074074
## iter 10 value 76.071231
## iter 20 value 71.110618
## iter 30 value 69.010607
## iter 40 value 68.620046
## iter 50 value 68.575700
## iter 60 value 68.571629
## iter 70 value 68.570057
## iter 80 value 68.569102
## final value 68.569063
## converged
## # weights: 109
## initial value 107.852459
## iter 10 value 59.224926
## iter 20 value 39.232537
## iter 30 value 36.123728
## iter 40 value 33.782363
## iter 50 value 33.393481
## iter 60 value 32.726052
## iter 70 value 31.822126
## iter 80 value 31.287364
## iter 90 value 30.988612
## iter 100 value 30.313712
## final value 30.313712
## stopped after 100 iterations
## # weights: 179
## initial value 122.246607
## iter 10 value 26.359998
## iter 20 value 16.447792
## iter 30 value 15.652143
## iter 40 value 15.491780
## iter 50 value 14.761786
## iter 60 value 14.753042
## iter 70 value 14.729587
## iter 80 value 12.982306
## iter 90 value 12.848875
## iter 100 value 12.844613
## final value 12.844613
## stopped after 100 iterations
## # weights: 39
## initial value 117.616004
## iter 10 value 70.091623
## iter 20 value 61.919012
## iter 30 value 60.350121
## iter 40 value 59.883131
## iter 50 value 59.557645
## iter 60 value 58.750542
## final value 58.738443
## converged
## # weights: 109
## initial value 110.271079
## iter 10 value 57.231152
## iter 20 value 29.598527
## iter 30 value 19.980459
## iter 40 value 17.459025
## iter 50 value 17.018922
## iter 60 value 16.885743
## iter 70 value 16.687703
## iter 80 value 16.614984
## iter 90 value 16.613148
## iter 90 value 16.613148
## iter 90 value 16.613148
## final value 16.613148
## converged
## # weights: 179
## initial value 108.148610
## iter 10 value 60.864418
## iter 20 value 26.107739
## iter 30 value 14.821464
## iter 40 value 12.936570
## iter 50 value 12.488586
## iter 60 value 12.399275
## iter 70 value 12.387366
## iter 80 value 12.384074
## iter 90 value 12.384002
## final value 12.384000
## converged
## # weights: 39
## initial value 99.672219
## iter 10 value 66.668741
## iter 20 value 56.541865
## iter 30 value 49.647541
## iter 40 value 46.983637
## iter 50 value 46.436520
## iter 60 value 45.291575
## iter 70 value 45.036242
## iter 80 value 45.023579
## iter 90 value 44.409968
## iter 100 value 42.803765
## final value 42.803765
## stopped after 100 iterations
## # weights: 109
## initial value 106.520235
## iter 10 value 39.520266
## iter 20 value 23.039909
## iter 30 value 15.670569
## iter 40 value 12.580631
## iter 50 value 9.704793
## iter 60 value 8.520926
## iter 70 value 8.176325
## iter 80 value 8.107813
## iter 90 value 7.792391
## iter 100 value 7.547020
## final value 7.547020
## stopped after 100 iterations
## # weights: 179
## initial value 94.639251
## iter 10 value 26.432558
## iter 20 value 11.694838
## iter 30 value 3.620085
## iter 40 value 2.811757
## iter 50 value 0.331177
## iter 60 value 0.255240
## iter 70 value 0.232505
## iter 80 value 0.219531
## iter 90 value 0.207239
## iter 100 value 0.188280
## final value 0.188280
## stopped after 100 iterations
## # weights: 39
## initial value 101.955099
## iter 10 value 58.547455
## iter 20 value 56.096916
## iter 30 value 55.576029
## iter 40 value 54.694568
## iter 50 value 54.276457
## iter 60 value 54.085698
## iter 70 value 54.055668
## iter 80 value 54.048491
## iter 90 value 54.047021
## final value 54.047018
## converged
## # weights: 109
## initial value 113.799929
## iter 10 value 50.605766
## iter 20 value 39.075051
## iter 30 value 15.877393
## iter 40 value 6.633401
## iter 50 value 5.607325
## iter 60 value 5.581716
## iter 70 value 5.581392
## iter 80 value 5.581202
## final value 5.581199
## converged
## # weights: 179
## initial value 102.433560
## iter 10 value 27.630212
## iter 20 value 8.169679
## iter 30 value 4.015954
## iter 40 value 3.823980
## iter 50 value 3.819553
## iter 60 value 3.819135
## iter 70 value 3.819110
## iter 80 value 3.819096
## final value 3.819095
## converged
## # weights: 39
## initial value 104.894651
## iter 10 value 67.222319
## iter 20 value 60.939577
## iter 30 value 58.827110
## iter 40 value 58.535081
## iter 50 value 58.276064
## iter 60 value 58.231354
## iter 70 value 57.967052
## final value 57.964173
## converged
## # weights: 109
## initial value 103.171180
## iter 10 value 53.155911
## iter 20 value 31.112623
## iter 30 value 19.107940
## iter 40 value 16.822459
## iter 50 value 16.317978
## iter 60 value 16.198758
## iter 70 value 15.755214
## iter 80 value 15.643074
## final value 15.642186
## converged
## # weights: 179
## initial value 126.716014
## iter 10 value 37.553336
## iter 20 value 18.808806
## iter 30 value 14.720767
## iter 40 value 12.824745
## iter 50 value 12.308859
## iter 60 value 11.965359
## iter 70 value 11.946654
## iter 80 value 11.945785
## final value 11.945778
## converged
## # weights: 39
## initial value 106.506137
## iter 10 value 63.483543
## iter 20 value 58.251297
## iter 30 value 56.306057
## iter 40 value 56.193543
## iter 50 value 55.944139
## iter 60 value 55.099118
## iter 70 value 54.762975
## iter 80 value 54.051386
## iter 90 value 53.406058
## iter 100 value 53.262717
## final value 53.262717
## stopped after 100 iterations
## # weights: 109
## initial value 100.743883
## iter 10 value 43.836289
## iter 20 value 28.798707
## iter 30 value 11.310145
## iter 40 value 6.674741
## iter 50 value 6.303535
## iter 60 value 6.045903
## iter 70 value 4.919794
## iter 80 value 4.645936
## iter 90 value 4.525052
## iter 100 value 4.330691
## final value 4.330691
## stopped after 100 iterations
## # weights: 179
## initial value 108.255633
## iter 10 value 37.536592
## iter 20 value 14.056476
## iter 30 value 10.499863
## iter 40 value 9.325585
## iter 50 value 9.181345
## iter 60 value 9.132988
## iter 70 value 9.111324
## iter 80 value 9.077657
## iter 90 value 8.993932
## iter 100 value 8.655349
## final value 8.655349
## stopped after 100 iterations
## # weights: 39
## initial value 113.983098
## iter 10 value 87.441127
## iter 20 value 79.352612
## iter 30 value 78.066712
## iter 40 value 76.671679
## iter 50 value 74.432980
## iter 60 value 73.952666
## iter 70 value 73.838108
## iter 80 value 73.819343
## iter 90 value 73.813846
## iter 100 value 73.812057
## final value 73.812057
## stopped after 100 iterations
## # weights: 109
## initial value 107.889333
## iter 10 value 40.854062
## iter 20 value 25.007905
## iter 30 value 19.239009
## iter 40 value 18.638793
## iter 50 value 18.518591
## iter 60 value 18.311121
## iter 70 value 17.976175
## iter 80 value 17.963642
## iter 90 value 17.959308
## iter 100 value 17.947421
## final value 17.947421
## stopped after 100 iterations
## # weights: 179
## initial value 113.977287
## iter 10 value 48.286552
## iter 20 value 20.015456
## iter 30 value 12.575581
## iter 40 value 7.679964
## iter 50 value 5.951701
## iter 60 value 4.206850
## iter 70 value 4.181075
## iter 80 value 4.172676
## iter 90 value 4.165681
## iter 100 value 4.164415
## final value 4.164415
## stopped after 100 iterations
## # weights: 39
## initial value 104.295824
## iter 10 value 75.210877
## iter 20 value 63.048352
## iter 30 value 58.488840
## iter 40 value 57.414507
## iter 50 value 57.394265
## final value 57.394232
## converged
## # weights: 109
## initial value 115.381280
## iter 10 value 48.512886
## iter 20 value 40.417664
## iter 30 value 33.541293
## iter 40 value 24.289052
## iter 50 value 18.641956
## iter 60 value 17.361724
## iter 70 value 17.056915
## iter 80 value 16.948316
## iter 90 value 16.947875
## final value 16.947873
## converged
## # weights: 179
## initial value 110.424543
## iter 10 value 31.157217
## iter 20 value 17.932690
## iter 30 value 14.527502
## iter 40 value 13.163494
## iter 50 value 12.950152
## iter 60 value 12.634455
## iter 70 value 12.527285
## iter 80 value 12.366793
## iter 90 value 12.354188
## iter 100 value 12.351244
## final value 12.351244
## stopped after 100 iterations
## # weights: 39
## initial value 104.133840
## iter 10 value 74.176563
## iter 20 value 62.253660
## iter 30 value 60.173528
## iter 40 value 57.933405
## iter 50 value 56.549344
## iter 60 value 52.659860
## iter 70 value 51.759682
## iter 80 value 51.599304
## iter 90 value 49.164642
## iter 100 value 48.935213
## final value 48.935213
## stopped after 100 iterations
## # weights: 109
## initial value 94.492022
## iter 10 value 37.476186
## iter 20 value 28.270609
## iter 30 value 26.324287
## iter 40 value 24.945171
## iter 50 value 23.881408
## iter 60 value 23.684189
## iter 70 value 23.582976
## iter 80 value 23.558483
## iter 90 value 23.493813
## iter 100 value 23.404815
## final value 23.404815
## stopped after 100 iterations
## # weights: 179
## initial value 103.884269
## iter 10 value 26.365540
## iter 20 value 7.869652
## iter 30 value 3.692560
## iter 40 value 3.132663
## iter 50 value 2.984652
## iter 60 value 2.932359
## iter 70 value 2.803554
## iter 80 value 2.447538
## iter 90 value 2.106101
## iter 100 value 2.094074
## final value 2.094074
## stopped after 100 iterations
## # weights: 39
## initial value 106.952758
## iter 10 value 72.637910
## iter 20 value 62.074466
## iter 30 value 58.996568
## iter 40 value 58.164663
## iter 50 value 58.095806
## iter 60 value 56.929749
## iter 70 value 55.880714
## iter 80 value 55.867418
## iter 90 value 55.839918
## iter 100 value 55.824794
## final value 55.824794
## stopped after 100 iterations
## # weights: 109
## initial value 105.912788
## iter 10 value 48.835434
## iter 20 value 25.377404
## iter 30 value 17.550656
## iter 40 value 12.536512
## iter 50 value 10.427239
## iter 60 value 9.741051
## iter 70 value 9.703370
## iter 80 value 9.675201
## iter 90 value 9.583550
## iter 100 value 9.580185
## final value 9.580185
## stopped after 100 iterations
## # weights: 179
## initial value 106.309196
## iter 10 value 26.936341
## iter 20 value 7.188706
## iter 30 value 2.016982
## iter 40 value 1.726869
## iter 50 value 1.394769
## iter 60 value 1.391318
## iter 70 value 1.388819
## iter 80 value 1.387589
## iter 90 value 1.387302
## iter 100 value 1.386701
## final value 1.386701
## stopped after 100 iterations
## # weights: 39
## initial value 106.758200
## iter 10 value 68.060533
## iter 20 value 58.827192
## iter 30 value 57.217470
## iter 40 value 56.801505
## iter 50 value 56.488998
## iter 60 value 56.361462
## iter 70 value 56.355441
## final value 56.355432
## converged
## # weights: 109
## initial value 104.347575
## iter 10 value 35.032481
## iter 20 value 19.578919
## iter 30 value 17.474573
## iter 40 value 17.185165
## iter 50 value 16.163480
## iter 60 value 15.653149
## iter 70 value 15.468152
## iter 80 value 14.948157
## iter 90 value 14.942889
## final value 14.942880
## converged
## # weights: 179
## initial value 116.380740
## iter 10 value 37.487523
## iter 20 value 17.297219
## iter 30 value 13.261150
## iter 40 value 11.801239
## iter 50 value 11.587774
## iter 60 value 11.573982
## iter 70 value 11.573726
## final value 11.573721
## converged
## # weights: 39
## initial value 110.591086
## iter 10 value 79.701509
## iter 20 value 66.051565
## iter 30 value 63.311775
## iter 40 value 60.188672
## iter 50 value 57.665496
## iter 60 value 56.028629
## iter 70 value 53.021750
## iter 80 value 52.640455
## iter 90 value 50.344370
## iter 100 value 49.760780
## final value 49.760780
## stopped after 100 iterations
## # weights: 109
## initial value 111.225988
## iter 10 value 55.032260
## iter 20 value 31.243901
## iter 30 value 21.242487
## iter 40 value 20.853761
## iter 50 value 20.505548
## iter 60 value 20.264127
## iter 70 value 19.970477
## iter 80 value 19.852801
## iter 90 value 19.734082
## iter 100 value 19.458771
## final value 19.458771
## stopped after 100 iterations
## # weights: 179
## initial value 108.085848
## iter 10 value 43.059980
## iter 20 value 4.622010
## iter 30 value 0.277884
## iter 40 value 0.233860
## iter 50 value 0.202374
## iter 60 value 0.177374
## iter 70 value 0.154542
## iter 80 value 0.146889
## iter 90 value 0.133110
## iter 100 value 0.128149
## final value 0.128149
## stopped after 100 iterations
## # weights: 39
## initial value 111.614371
## iter 10 value 67.853567
## iter 20 value 56.814323
## iter 30 value 53.091755
## iter 40 value 53.069044
## iter 50 value 53.067391
## iter 60 value 53.067199
## iter 70 value 53.067139
## final value 53.067093
## converged
## # weights: 109
## initial value 114.080231
## iter 10 value 58.374017
## iter 20 value 42.452250
## iter 30 value 29.675842
## iter 40 value 25.644864
## iter 50 value 25.331905
## iter 60 value 21.325415
## iter 70 value 20.303688
## iter 80 value 20.264718
## iter 90 value 20.263296
## iter 100 value 20.263049
## final value 20.263049
## stopped after 100 iterations
## # weights: 179
## initial value 105.586240
## iter 10 value 18.053246
## iter 20 value 5.637791
## iter 30 value 0.318545
## iter 40 value 0.009110
## iter 50 value 0.000690
## iter 60 value 0.000199
## iter 70 value 0.000102
## final value 0.000100
## converged
## # weights: 39
## initial value 103.785528
## iter 10 value 67.873462
## iter 20 value 63.564800
## iter 30 value 61.075913
## iter 40 value 60.650053
## iter 50 value 60.141771
## iter 60 value 58.677672
## iter 70 value 58.158835
## iter 80 value 57.980951
## iter 90 value 57.961033
## iter 90 value 57.961032
## iter 90 value 57.961032
## final value 57.961032
## converged
## # weights: 109
## initial value 116.856886
## iter 10 value 47.658898
## iter 20 value 31.980101
## iter 30 value 20.532882
## iter 40 value 18.388903
## iter 50 value 17.760540
## iter 60 value 17.506323
## iter 70 value 16.928798
## iter 80 value 16.607733
## iter 90 value 16.529480
## iter 100 value 16.513202
## final value 16.513202
## stopped after 100 iterations
## # weights: 179
## initial value 125.097607
## iter 10 value 54.197389
## iter 20 value 30.854924
## iter 30 value 21.855689
## iter 40 value 16.674901
## iter 50 value 14.312230
## iter 60 value 12.945907
## iter 70 value 12.813269
## iter 80 value 12.806684
## final value 12.806602
## converged
## # weights: 39
## initial value 109.454281
## iter 10 value 68.489554
## iter 20 value 58.051799
## iter 30 value 53.950597
## iter 40 value 50.492012
## iter 50 value 50.267121
## iter 60 value 50.249982
## iter 70 value 50.183703
## iter 80 value 50.142698
## iter 90 value 50.139171
## iter 100 value 50.124238
## final value 50.124238
## stopped after 100 iterations
## # weights: 109
## initial value 104.158381
## iter 10 value 29.158508
## iter 20 value 15.290944
## iter 30 value 13.104837
## iter 40 value 12.508508
## iter 50 value 12.405101
## iter 60 value 12.357995
## iter 70 value 11.930820
## iter 80 value 11.809774
## iter 90 value 11.799290
## iter 100 value 11.752049
## final value 11.752049
## stopped after 100 iterations
## # weights: 179
## initial value 126.750437
## iter 10 value 62.013794
## iter 20 value 56.182839
## iter 30 value 53.208551
## iter 40 value 52.784011
## iter 50 value 52.647743
## iter 60 value 52.524650
## iter 70 value 52.113119
## iter 80 value 51.706642
## iter 90 value 49.014534
## iter 100 value 48.602914
## final value 48.602914
## stopped after 100 iterations
## # weights: 39
## initial value 102.783425
## iter 10 value 63.566598
## iter 20 value 59.943813
## iter 30 value 59.019441
## iter 40 value 58.120556
## iter 50 value 52.672600
## iter 60 value 51.869770
## iter 70 value 50.868868
## iter 80 value 50.633839
## iter 90 value 49.154694
## iter 100 value 48.850587
## final value 48.850587
## stopped after 100 iterations
## # weights: 109
## initial value 101.225242
## iter 10 value 56.818837
## iter 20 value 30.455290
## iter 30 value 17.238070
## iter 40 value 8.748656
## iter 50 value 8.702953
## iter 60 value 8.670252
## iter 70 value 8.037576
## iter 80 value 6.758811
## iter 90 value 6.753019
## iter 100 value 6.455886
## final value 6.455886
## stopped after 100 iterations
## # weights: 179
## initial value 138.253773
## iter 10 value 47.350613
## iter 20 value 32.195540
## iter 30 value 18.590606
## iter 40 value 17.754454
## iter 50 value 17.452345
## iter 60 value 17.294296
## iter 70 value 11.425029
## iter 80 value 7.279258
## iter 90 value 6.712674
## iter 100 value 6.675366
## final value 6.675366
## stopped after 100 iterations
## # weights: 39
## initial value 105.595506
## iter 10 value 70.007793
## iter 20 value 63.735123
## iter 30 value 60.656865
## iter 40 value 58.436173
## iter 50 value 58.376796
## final value 58.376740
## converged
## # weights: 109
## initial value 104.408190
## iter 10 value 54.812636
## iter 20 value 33.437008
## iter 30 value 26.143288
## iter 40 value 19.021726
## iter 50 value 16.400660
## iter 60 value 15.749509
## iter 70 value 15.735730
## iter 80 value 15.734676
## final value 15.734656
## converged
## # weights: 179
## initial value 144.306136
## iter 10 value 67.928940
## iter 20 value 34.866184
## iter 30 value 21.767935
## iter 40 value 15.933002
## iter 50 value 13.188491
## iter 60 value 11.904032
## iter 70 value 11.687760
## iter 80 value 11.676570
## iter 90 value 11.676338
## final value 11.676334
## converged
## # weights: 39
## initial value 100.249235
## iter 10 value 79.316363
## iter 20 value 54.146303
## iter 30 value 51.926509
## iter 40 value 50.587084
## iter 50 value 50.489573
## iter 60 value 50.474210
## iter 70 value 50.458626
## iter 80 value 50.450965
## iter 90 value 50.444014
## iter 100 value 50.426680
## final value 50.426680
## stopped after 100 iterations
## # weights: 109
## initial value 102.457680
## iter 10 value 30.173731
## iter 20 value 23.577067
## iter 30 value 19.575446
## iter 40 value 18.388200
## iter 50 value 18.086073
## iter 60 value 17.781574
## iter 70 value 17.762747
## iter 80 value 17.523411
## iter 90 value 17.007560
## iter 100 value 16.837784
## final value 16.837784
## stopped after 100 iterations
## # weights: 179
## initial value 107.763843
## iter 10 value 12.222938
## iter 20 value 4.833238
## iter 30 value 2.547389
## iter 40 value 2.506760
## iter 50 value 2.465933
## iter 60 value 2.446618
## iter 70 value 2.429134
## iter 80 value 2.414251
## iter 90 value 2.039472
## iter 100 value 0.211514
## final value 0.211514
## stopped after 100 iterations
## # weights: 39
## initial value 104.472068
## iter 10 value 65.134966
## iter 20 value 58.039746
## iter 30 value 51.529865
## iter 40 value 46.482814
## iter 50 value 45.560272
## iter 60 value 45.233478
## iter 70 value 45.166909
## iter 80 value 45.128088
## iter 90 value 45.123443
## iter 100 value 45.121269
## final value 45.121269
## stopped after 100 iterations
## # weights: 109
## initial value 98.200797
## iter 10 value 22.463152
## iter 20 value 12.801040
## iter 30 value 8.665100
## iter 40 value 8.131949
## iter 50 value 6.254632
## iter 60 value 3.792581
## iter 70 value 3.664422
## iter 80 value 3.575235
## iter 90 value 3.497066
## iter 100 value 3.469725
## final value 3.469725
## stopped after 100 iterations
## # weights: 179
## initial value 106.456868
## iter 10 value 19.267289
## iter 20 value 4.937744
## iter 30 value 2.268550
## iter 40 value 2.250703
## iter 50 value 2.249501
## iter 60 value 2.249382
## iter 70 value 2.249351
## final value 2.249341
## converged
## # weights: 39
## initial value 103.966156
## iter 10 value 67.868376
## iter 20 value 63.303665
## iter 30 value 61.969825
## iter 40 value 61.070824
## iter 50 value 60.274006
## iter 60 value 58.627562
## iter 70 value 57.704211
## iter 80 value 57.312106
## iter 90 value 57.223031
## iter 100 value 57.126860
## final value 57.126860
## stopped after 100 iterations
## # weights: 109
## initial value 103.489716
## iter 10 value 56.695186
## iter 20 value 34.519226
## iter 30 value 21.327647
## iter 40 value 19.725156
## iter 50 value 19.147502
## iter 60 value 18.786956
## iter 70 value 16.128017
## iter 80 value 16.006829
## iter 90 value 16.004455
## final value 16.004440
## converged
## # weights: 179
## initial value 127.364733
## iter 10 value 55.537825
## iter 20 value 35.271283
## iter 30 value 19.268067
## iter 40 value 16.335774
## iter 50 value 14.685337
## iter 60 value 12.903359
## iter 70 value 12.752867
## iter 80 value 12.577488
## iter 90 value 12.198383
## iter 100 value 12.116167
## final value 12.116167
## stopped after 100 iterations
## # weights: 39
## initial value 109.354850
## iter 10 value 67.274959
## iter 20 value 61.111110
## iter 30 value 59.295381
## iter 40 value 58.688705
## iter 50 value 58.659685
## iter 60 value 58.177106
## iter 70 value 57.532967
## iter 80 value 57.032534
## iter 90 value 56.098088
## iter 100 value 55.168180
## final value 55.168180
## stopped after 100 iterations
## # weights: 109
## initial value 106.031369
## iter 10 value 33.048027
## iter 20 value 18.252925
## iter 30 value 13.226640
## iter 40 value 6.668093
## iter 50 value 5.952012
## iter 60 value 5.894128
## iter 70 value 5.855142
## iter 80 value 5.834063
## iter 90 value 5.652256
## iter 100 value 3.916099
## final value 3.916099
## stopped after 100 iterations
## # weights: 179
## initial value 113.749683
## iter 10 value 37.107881
## iter 20 value 9.860052
## iter 30 value 4.308719
## iter 40 value 3.625436
## iter 50 value 3.538512
## iter 60 value 1.664658
## iter 70 value 1.603890
## iter 80 value 1.589119
## iter 90 value 1.565081
## iter 100 value 1.241274
## final value 1.241274
## stopped after 100 iterations
## # weights: 39
## initial value 110.703885
## iter 10 value 80.488171
## iter 20 value 58.409982
## iter 30 value 55.031395
## iter 40 value 54.701676
## iter 50 value 54.433406
## iter 60 value 54.427639
## iter 70 value 54.390768
## iter 80 value 53.572150
## iter 90 value 53.380987
## iter 100 value 52.607142
## final value 52.607142
## stopped after 100 iterations
## # weights: 109
## initial value 112.777157
## iter 10 value 35.777908
## iter 20 value 14.676191
## iter 30 value 8.127686
## iter 40 value 6.662475
## iter 50 value 6.064834
## iter 60 value 5.670813
## iter 70 value 3.350123
## iter 80 value 3.297484
## iter 90 value 3.296396
## iter 100 value 3.295869
## final value 3.295869
## stopped after 100 iterations
## # weights: 179
## initial value 114.786911
## iter 10 value 37.376245
## iter 20 value 31.019746
## iter 30 value 30.268802
## iter 40 value 29.571426
## iter 50 value 28.070072
## iter 60 value 27.151458
## iter 70 value 26.394883
## iter 80 value 24.582419
## iter 90 value 24.321786
## iter 100 value 24.319401
## final value 24.319401
## stopped after 100 iterations
## # weights: 39
## initial value 102.442172
## iter 10 value 77.450999
## iter 20 value 71.361642
## iter 30 value 65.028247
## iter 40 value 59.644186
## iter 50 value 58.821794
## iter 60 value 58.733069
## iter 70 value 58.293805
## iter 80 value 58.185517
## iter 90 value 58.183982
## iter 90 value 58.183981
## iter 90 value 58.183981
## final value 58.183981
## converged
## # weights: 109
## initial value 117.453113
## iter 10 value 51.873481
## iter 20 value 30.692088
## iter 30 value 21.786803
## iter 40 value 17.152029
## iter 50 value 16.807359
## iter 60 value 16.761771
## iter 70 value 16.760704
## final value 16.760703
## converged
## # weights: 179
## initial value 97.760439
## iter 10 value 40.313885
## iter 20 value 18.795398
## iter 30 value 14.296333
## iter 40 value 12.979477
## iter 50 value 12.715987
## iter 60 value 12.525710
## iter 70 value 12.495610
## iter 80 value 12.494957
## final value 12.494950
## converged
## # weights: 39
## initial value 106.384231
## iter 10 value 86.192459
## iter 20 value 64.956486
## iter 30 value 63.513229
## iter 40 value 62.674825
## iter 50 value 62.053816
## iter 60 value 62.050996
## iter 70 value 62.036129
## iter 80 value 62.029184
## iter 90 value 62.025271
## iter 100 value 60.799769
## final value 60.799769
## stopped after 100 iterations
## # weights: 109
## initial value 105.295497
## iter 10 value 49.988130
## iter 20 value 35.420163
## iter 30 value 11.650890
## iter 40 value 2.990496
## iter 50 value 0.816110
## iter 60 value 0.689317
## iter 70 value 0.591054
## iter 80 value 0.499476
## iter 90 value 0.468056
## iter 100 value 0.428906
## final value 0.428906
## stopped after 100 iterations
## # weights: 179
## initial value 96.208628
## iter 10 value 22.444190
## iter 20 value 6.249756
## iter 30 value 4.750947
## iter 40 value 4.134210
## iter 50 value 3.953893
## iter 60 value 3.583218
## iter 70 value 3.565411
## iter 80 value 3.527629
## iter 90 value 3.495434
## iter 100 value 3.454413
## final value 3.454413
## stopped after 100 iterations
## # weights: 39
## initial value 99.113616
## iter 10 value 72.921939
## iter 20 value 65.653703
## iter 30 value 65.557822
## iter 40 value 65.356096
## iter 50 value 64.570840
## iter 60 value 64.547269
## iter 70 value 64.545236
## iter 80 value 64.540920
## iter 90 value 64.540150
## iter 90 value 64.540149
## iter 90 value 64.540149
## final value 64.540149
## converged
## # weights: 109
## initial value 100.265026
## iter 10 value 32.744203
## iter 20 value 9.347204
## iter 30 value 6.459242
## iter 40 value 6.445520
## iter 50 value 6.435164
## iter 60 value 6.108671
## final value 6.108652
## converged
## # weights: 179
## initial value 107.617677
## iter 10 value 20.094854
## iter 20 value 4.197975
## iter 30 value 2.568063
## iter 40 value 2.503684
## iter 50 value 2.502390
## iter 60 value 2.502017
## iter 70 value 2.502013
## iter 70 value 2.502013
## iter 70 value 2.502013
## final value 2.502013
## converged
## # weights: 39
## initial value 101.922734
## iter 10 value 67.704235
## iter 20 value 60.938457
## iter 30 value 60.304342
## iter 40 value 57.859377
## iter 50 value 57.228095
## iter 60 value 57.071092
## iter 70 value 56.961622
## iter 80 value 56.853297
## iter 90 value 56.844005
## iter 100 value 56.090008
## final value 56.090008
## stopped after 100 iterations
## # weights: 109
## initial value 114.608044
## iter 10 value 48.681598
## iter 20 value 27.725466
## iter 30 value 20.921050
## iter 40 value 17.400910
## iter 50 value 16.832208
## iter 60 value 16.247031
## iter 70 value 15.821144
## iter 80 value 15.776211
## iter 90 value 15.775778
## final value 15.775777
## converged
## # weights: 179
## initial value 108.577470
## iter 10 value 45.492580
## iter 20 value 19.062566
## iter 30 value 13.571778
## iter 40 value 12.130881
## iter 50 value 11.818093
## iter 60 value 11.708886
## iter 70 value 11.704983
## iter 80 value 11.704270
## iter 90 value 11.704177
## final value 11.704174
## converged
## # weights: 39
## initial value 118.384405
## iter 10 value 63.061188
## iter 20 value 56.866506
## iter 30 value 52.918963
## iter 40 value 48.782648
## iter 50 value 47.685378
## iter 60 value 46.647753
## iter 70 value 46.547462
## iter 80 value 46.498774
## iter 90 value 46.468622
## iter 100 value 46.441613
## final value 46.441613
## stopped after 100 iterations
## # weights: 109
## initial value 100.523905
## iter 10 value 49.667985
## iter 20 value 32.272071
## iter 30 value 28.077417
## iter 40 value 24.766138
## iter 50 value 22.748339
## iter 60 value 17.032807
## iter 70 value 16.071974
## iter 80 value 14.313349
## iter 90 value 12.176478
## iter 100 value 8.352725
## final value 8.352725
## stopped after 100 iterations
## # weights: 179
## initial value 110.598998
## iter 10 value 16.468345
## iter 20 value 6.948654
## iter 30 value 5.736880
## iter 40 value 5.412080
## iter 50 value 2.782083
## iter 60 value 2.072471
## iter 70 value 2.063814
## iter 80 value 2.035121
## iter 90 value 2.021741
## iter 100 value 1.508672
## final value 1.508672
## stopped after 100 iterations
## # weights: 39
## initial value 107.272666
## iter 10 value 82.800546
## iter 20 value 60.398541
## iter 30 value 54.001731
## iter 40 value 50.788222
## iter 50 value 44.272932
## iter 60 value 41.151994
## iter 70 value 40.323200
## iter 80 value 39.398915
## iter 90 value 38.896513
## iter 100 value 38.652505
## final value 38.652505
## stopped after 100 iterations
## # weights: 109
## initial value 100.410174
## iter 10 value 50.415440
## iter 20 value 32.138839
## iter 30 value 24.851638
## iter 40 value 19.395540
## iter 50 value 16.170443
## iter 60 value 14.617797
## iter 70 value 14.250443
## iter 80 value 14.179781
## iter 90 value 12.872134
## iter 100 value 12.302557
## final value 12.302557
## stopped after 100 iterations
## # weights: 179
## initial value 118.142310
## iter 10 value 30.004293
## iter 20 value 7.019661
## iter 30 value 5.051398
## iter 40 value 3.789894
## iter 50 value 3.669587
## iter 60 value 3.648646
## iter 70 value 1.503194
## iter 80 value 1.415382
## iter 90 value 1.389269
## iter 100 value 1.387706
## final value 1.387706
## stopped after 100 iterations
## # weights: 39
## initial value 97.008143
## iter 10 value 72.247719
## iter 20 value 62.286870
## iter 30 value 59.569785
## iter 40 value 59.020275
## iter 50 value 57.718665
## iter 60 value 57.447456
## iter 70 value 57.445067
## final value 57.445063
## converged
## # weights: 109
## initial value 124.568072
## iter 10 value 62.224584
## iter 20 value 37.463481
## iter 30 value 23.894316
## iter 40 value 18.082286
## iter 50 value 17.401659
## iter 60 value 17.057359
## iter 70 value 16.524935
## iter 80 value 16.427424
## iter 90 value 16.422662
## iter 100 value 16.421379
## final value 16.421379
## stopped after 100 iterations
## # weights: 179
## initial value 114.851308
## iter 10 value 53.512838
## iter 20 value 25.344277
## iter 30 value 14.305521
## iter 40 value 13.132389
## iter 50 value 12.439838
## iter 60 value 12.248077
## iter 70 value 12.170287
## iter 80 value 12.157901
## iter 90 value 12.154942
## iter 100 value 12.154343
## final value 12.154343
## stopped after 100 iterations
## # weights: 39
## initial value 102.543893
## iter 10 value 67.145239
## iter 20 value 59.782776
## iter 30 value 57.414551
## iter 40 value 56.409475
## iter 50 value 55.935739
## iter 60 value 55.707121
## iter 70 value 54.681393
## iter 80 value 54.117766
## iter 90 value 53.981116
## iter 100 value 53.948032
## final value 53.948032
## stopped after 100 iterations
## # weights: 109
## initial value 109.860170
## iter 10 value 39.899647
## iter 20 value 16.202797
## iter 30 value 11.704230
## iter 40 value 11.233258
## iter 50 value 11.019401
## iter 60 value 10.904538
## iter 70 value 10.879961
## iter 80 value 6.865715
## iter 90 value 3.766429
## iter 100 value 0.626508
## final value 0.626508
## stopped after 100 iterations
## # weights: 179
## initial value 112.124048
## iter 10 value 24.134817
## iter 20 value 9.305798
## iter 30 value 6.521452
## iter 40 value 6.345559
## iter 50 value 6.243299
## iter 60 value 5.923178
## iter 70 value 5.867427
## iter 80 value 5.829981
## iter 90 value 5.767820
## iter 100 value 5.182955
## final value 5.182955
## stopped after 100 iterations
## # weights: 39
## initial value 110.634912
## iter 10 value 68.182465
## iter 20 value 56.377025
## iter 30 value 52.504642
## iter 40 value 52.058772
## iter 50 value 52.026438
## iter 60 value 52.021982
## iter 70 value 50.954103
## iter 80 value 50.921102
## iter 90 value 50.920844
## final value 50.920754
## converged
## # weights: 109
## initial value 113.358653
## iter 10 value 38.396240
## iter 20 value 14.639965
## iter 30 value 10.721241
## iter 40 value 7.707064
## iter 50 value 7.699491
## iter 60 value 7.544418
## iter 70 value 7.069264
## iter 80 value 6.214987
## iter 90 value 6.214747
## iter 100 value 6.214670
## final value 6.214670
## stopped after 100 iterations
## # weights: 179
## initial value 107.994989
## iter 10 value 13.797898
## iter 20 value 2.024231
## iter 30 value 1.396996
## iter 40 value 1.393423
## iter 50 value 1.390854
## iter 60 value 1.389382
## iter 70 value 1.388439
## iter 80 value 1.387100
## iter 90 value 1.386784
## iter 100 value 1.385519
## final value 1.385519
## stopped after 100 iterations
## # weights: 39
## initial value 114.138293
## iter 10 value 68.792427
## iter 20 value 65.723801
## iter 30 value 64.272385
## iter 40 value 62.146904
## iter 50 value 60.602517
## iter 60 value 60.202512
## iter 70 value 60.199203
## final value 60.199192
## converged
## # weights: 109
## initial value 105.992930
## iter 10 value 53.257733
## iter 20 value 32.496133
## iter 30 value 22.352638
## iter 40 value 17.205258
## iter 50 value 16.997841
## iter 60 value 16.760333
## iter 70 value 16.740466
## final value 16.740360
## converged
## # weights: 179
## initial value 105.287521
## iter 10 value 46.482191
## iter 20 value 20.111151
## iter 30 value 14.254839
## iter 40 value 13.770123
## iter 50 value 13.301561
## iter 60 value 12.872573
## iter 70 value 12.610167
## iter 80 value 12.587757
## iter 90 value 12.581930
## iter 100 value 12.580992
## final value 12.580992
## stopped after 100 iterations
## # weights: 39
## initial value 100.356291
## iter 10 value 63.710645
## iter 20 value 59.115044
## iter 30 value 57.022947
## iter 40 value 52.301288
## iter 50 value 50.694020
## iter 60 value 50.668459
## iter 70 value 50.654261
## iter 80 value 50.650035
## iter 90 value 50.638299
## iter 100 value 50.609467
## final value 50.609467
## stopped after 100 iterations
## # weights: 109
## initial value 114.680992
## iter 10 value 32.293269
## iter 20 value 19.073418
## iter 30 value 12.460581
## iter 40 value 11.133689
## iter 50 value 10.905725
## iter 60 value 9.730944
## iter 70 value 7.168528
## iter 80 value 6.691988
## iter 90 value 5.177774
## iter 100 value 3.450838
## final value 3.450838
## stopped after 100 iterations
## # weights: 179
## initial value 107.312952
## iter 10 value 46.890948
## iter 20 value 26.889055
## iter 30 value 10.683660
## iter 40 value 7.244474
## iter 50 value 4.646879
## iter 60 value 3.150502
## iter 70 value 3.090480
## iter 80 value 2.910310
## iter 90 value 2.710256
## iter 100 value 2.474370
## final value 2.474370
## stopped after 100 iterations
## # weights: 39
## initial value 101.962831
## iter 10 value 61.687403
## iter 20 value 49.229500
## iter 30 value 47.237994
## iter 40 value 43.575673
## iter 50 value 42.249380
## iter 60 value 41.237261
## iter 70 value 40.478574
## iter 80 value 40.271618
## iter 90 value 40.174061
## iter 100 value 39.778957
## final value 39.778957
## stopped after 100 iterations
## # weights: 109
## initial value 121.703694
## iter 10 value 34.681561
## iter 20 value 18.935310
## iter 30 value 13.556597
## iter 40 value 9.527864
## iter 50 value 8.961855
## iter 60 value 7.128974
## iter 70 value 7.025292
## iter 80 value 6.867734
## iter 90 value 6.832520
## iter 100 value 4.161550
## final value 4.161550
## stopped after 100 iterations
## # weights: 179
## initial value 107.825514
## iter 10 value 19.419906
## iter 20 value 4.785055
## iter 30 value 3.048851
## iter 40 value 2.986383
## iter 50 value 2.874263
## iter 60 value 2.872770
## iter 70 value 2.872438
## iter 80 value 2.871881
## iter 90 value 2.871440
## iter 100 value 2.870836
## final value 2.870836
## stopped after 100 iterations
## # weights: 39
## initial value 103.157614
## iter 10 value 79.007399
## iter 20 value 63.890503
## iter 30 value 57.914178
## iter 40 value 57.381019
## iter 50 value 57.274709
## iter 60 value 57.274391
## iter 70 value 57.273781
## iter 80 value 57.225607
## iter 90 value 57.010656
## iter 100 value 56.997699
## final value 56.997699
## stopped after 100 iterations
## # weights: 109
## initial value 129.036942
## iter 10 value 90.194555
## iter 20 value 53.933354
## iter 30 value 31.591529
## iter 40 value 23.068586
## iter 50 value 21.534505
## iter 60 value 19.909000
## iter 70 value 19.499699
## iter 80 value 17.477444
## iter 90 value 17.073724
## iter 100 value 16.868708
## final value 16.868708
## stopped after 100 iterations
## # weights: 179
## initial value 117.924008
## iter 10 value 40.720678
## iter 20 value 19.860861
## iter 30 value 14.092558
## iter 40 value 13.261610
## iter 50 value 12.914291
## iter 60 value 12.834171
## iter 70 value 12.536180
## iter 80 value 12.278690
## iter 90 value 12.145686
## iter 100 value 12.130243
## final value 12.130243
## stopped after 100 iterations
## # weights: 39
## initial value 105.515112
## iter 10 value 64.161986
## iter 20 value 58.085071
## iter 30 value 55.082193
## iter 40 value 53.278238
## iter 50 value 52.878081
## iter 60 value 52.718081
## iter 70 value 51.289597
## iter 80 value 51.239049
## iter 90 value 50.490675
## iter 100 value 50.476290
## final value 50.476290
## stopped after 100 iterations
## # weights: 109
## initial value 107.663158
## iter 10 value 30.395845
## iter 20 value 16.338048
## iter 30 value 10.750979
## iter 40 value 9.986752
## iter 50 value 9.912210
## iter 60 value 9.887517
## iter 70 value 9.872414
## iter 80 value 6.306138
## iter 90 value 6.138391
## iter 100 value 6.072247
## final value 6.072247
## stopped after 100 iterations
## # weights: 179
## initial value 108.517010
## iter 10 value 17.789334
## iter 20 value 3.311656
## iter 30 value 2.230421
## iter 40 value 2.189777
## iter 50 value 2.159703
## iter 60 value 2.131966
## iter 70 value 2.106793
## iter 80 value 2.091404
## iter 90 value 2.076319
## iter 100 value 2.065186
## final value 2.065186
## stopped after 100 iterations
## # weights: 39
## initial value 108.925966
## iter 10 value 72.821162
## iter 20 value 61.877328
## iter 30 value 57.854829
## iter 40 value 56.727041
## iter 50 value 53.732413
## iter 60 value 52.935870
## iter 70 value 52.856231
## iter 80 value 52.832364
## iter 90 value 52.819567
## iter 100 value 52.813238
## final value 52.813238
## stopped after 100 iterations
## # weights: 109
## initial value 106.074296
## iter 10 value 49.431645
## iter 20 value 29.766269
## iter 30 value 26.193697
## iter 40 value 25.361985
## iter 50 value 22.369342
## iter 60 value 21.290062
## iter 70 value 19.579892
## iter 80 value 19.495383
## iter 90 value 19.420450
## iter 100 value 19.352546
## final value 19.352546
## stopped after 100 iterations
## # weights: 179
## initial value 94.748098
## iter 10 value 18.101053
## iter 20 value 5.988878
## iter 30 value 2.600707
## iter 40 value 2.520008
## iter 50 value 2.503619
## iter 60 value 2.502046
## iter 70 value 2.502038
## final value 2.502015
## converged
## # weights: 39
## initial value 100.336601
## iter 10 value 77.676649
## iter 20 value 65.451032
## iter 30 value 60.880861
## iter 40 value 59.277350
## iter 50 value 58.744266
## iter 60 value 58.620230
## final value 58.619144
## converged
## # weights: 109
## initial value 105.408318
## iter 10 value 60.048458
## iter 20 value 40.369959
## iter 30 value 22.700958
## iter 40 value 19.381992
## iter 50 value 17.120847
## iter 60 value 15.918277
## iter 70 value 15.797804
## iter 80 value 15.797072
## final value 15.797070
## converged
## # weights: 179
## initial value 108.402575
## iter 10 value 46.928294
## iter 20 value 22.986062
## iter 30 value 14.016179
## iter 40 value 12.312959
## iter 50 value 12.193953
## iter 60 value 12.067436
## iter 70 value 12.032158
## iter 80 value 12.026064
## iter 90 value 12.025564
## iter 100 value 12.025479
## final value 12.025479
## stopped after 100 iterations
## # weights: 39
## initial value 106.397803
## iter 10 value 74.343234
## iter 20 value 69.506666
## iter 30 value 69.422174
## iter 40 value 69.295797
## iter 50 value 68.018937
## iter 60 value 67.694800
## iter 70 value 67.366701
## iter 80 value 67.333162
## iter 90 value 67.257467
## iter 100 value 67.231199
## final value 67.231199
## stopped after 100 iterations
## # weights: 109
## initial value 99.335807
## iter 10 value 36.136154
## iter 20 value 22.185656
## iter 30 value 11.520506
## iter 40 value 11.246051
## iter 50 value 10.685919
## iter 60 value 9.930926
## iter 70 value 9.897390
## iter 80 value 9.872438
## iter 90 value 9.862833
## iter 100 value 9.752396
## final value 9.752396
## stopped after 100 iterations
## # weights: 179
## initial value 103.370245
## iter 10 value 17.600029
## iter 20 value 0.818697
## iter 30 value 0.209391
## iter 40 value 0.185497
## iter 50 value 0.167523
## iter 60 value 0.152191
## iter 70 value 0.142294
## iter 80 value 0.134822
## iter 90 value 0.126952
## iter 100 value 0.116865
## final value 0.116865
## stopped after 100 iterations
## # weights: 39
## initial value 118.386989
## iter 10 value 70.272344
## iter 20 value 63.872142
## iter 30 value 61.895551
## iter 40 value 59.146511
## iter 50 value 58.699191
## iter 60 value 58.658716
## iter 70 value 58.453737
## iter 80 value 56.263117
## iter 90 value 54.165104
## iter 100 value 53.238441
## final value 53.238441
## stopped after 100 iterations
## # weights: 109
## initial value 124.802183
## iter 10 value 66.778583
## iter 20 value 44.573250
## iter 30 value 36.478326
## iter 40 value 35.292182
## iter 50 value 34.900753
## iter 60 value 33.879169
## iter 70 value 32.656809
## iter 80 value 31.116920
## iter 90 value 30.363885
## iter 100 value 28.642064
## final value 28.642064
## stopped after 100 iterations
## # weights: 179
## initial value 117.588671
## iter 10 value 27.697363
## iter 20 value 13.736849
## iter 30 value 10.406578
## iter 40 value 7.983207
## iter 50 value 7.709185
## iter 60 value 6.890202
## iter 70 value 6.813163
## iter 80 value 6.803217
## iter 90 value 6.799305
## iter 100 value 6.798344
## final value 6.798344
## stopped after 100 iterations
## # weights: 39
## initial value 103.102643
## iter 10 value 76.090873
## iter 20 value 68.032499
## iter 30 value 63.185168
## iter 40 value 56.705649
## iter 50 value 55.388693
## iter 60 value 55.286144
## iter 70 value 55.283618
## final value 55.283611
## converged
## # weights: 109
## initial value 102.712415
## iter 10 value 53.974792
## iter 20 value 33.187841
## iter 30 value 20.496184
## iter 40 value 16.985953
## iter 50 value 16.768001
## iter 60 value 16.717314
## iter 70 value 16.713962
## final value 16.713956
## converged
## # weights: 179
## initial value 117.899601
## iter 10 value 42.196434
## iter 20 value 21.850921
## iter 30 value 16.212846
## iter 40 value 13.845541
## iter 50 value 13.620207
## iter 60 value 13.570854
## iter 70 value 13.508730
## iter 80 value 13.508508
## iter 80 value 13.508508
## iter 80 value 13.508508
## final value 13.508508
## converged
## # weights: 39
## initial value 98.204453
## iter 10 value 66.925777
## iter 20 value 62.607699
## iter 30 value 60.918802
## iter 40 value 59.954112
## iter 50 value 57.280459
## iter 60 value 54.676086
## iter 70 value 52.782648
## iter 80 value 50.192457
## iter 90 value 50.087299
## iter 100 value 50.060979
## final value 50.060979
## stopped after 100 iterations
## # weights: 109
## initial value 105.241956
## iter 10 value 33.231360
## iter 20 value 12.857632
## iter 30 value 12.093625
## iter 40 value 11.859150
## iter 50 value 11.594309
## iter 60 value 11.401037
## iter 70 value 10.979110
## iter 80 value 10.572296
## iter 90 value 10.214139
## iter 100 value 10.068251
## final value 10.068251
## stopped after 100 iterations
## # weights: 179
## initial value 102.444457
## iter 10 value 31.587908
## iter 20 value 11.404162
## iter 30 value 8.266415
## iter 40 value 6.359100
## iter 50 value 3.799604
## iter 60 value 2.215690
## iter 70 value 1.574195
## iter 80 value 0.574066
## iter 90 value 0.382815
## iter 100 value 0.344822
## final value 0.344822
## stopped after 100 iterations
## # weights: 39
## initial value 98.605406
## iter 10 value 57.210617
## iter 20 value 52.106117
## iter 30 value 46.303226
## iter 40 value 41.827344
## iter 50 value 40.113769
## iter 60 value 40.058623
## iter 70 value 39.379092
## iter 80 value 39.238122
## iter 90 value 39.237061
## iter 100 value 39.235785
## final value 39.235785
## stopped after 100 iterations
## # weights: 109
## initial value 106.906179
## iter 10 value 54.206539
## iter 20 value 21.737477
## iter 30 value 12.047799
## iter 40 value 6.739778
## iter 50 value 4.526489
## iter 60 value 3.734927
## iter 70 value 3.627303
## iter 80 value 3.170903
## iter 90 value 2.640689
## iter 100 value 2.585735
## final value 2.585735
## stopped after 100 iterations
## # weights: 179
## initial value 112.221956
## iter 10 value 46.916463
## iter 20 value 40.083478
## iter 30 value 38.383812
## iter 40 value 36.751142
## iter 50 value 33.822764
## iter 60 value 32.927826
## iter 70 value 31.204076
## iter 80 value 25.572414
## iter 90 value 22.977663
## iter 100 value 19.680016
## final value 19.680016
## stopped after 100 iterations
## # weights: 39
## initial value 107.494255
## iter 10 value 82.514884
## iter 20 value 67.480589
## iter 30 value 59.585368
## iter 40 value 56.144077
## iter 50 value 55.831848
## iter 60 value 55.823251
## final value 55.823237
## converged
## # weights: 109
## initial value 113.692212
## iter 10 value 37.322898
## iter 20 value 23.714628
## iter 30 value 17.691700
## iter 40 value 16.320294
## iter 50 value 15.954147
## iter 60 value 15.930955
## iter 70 value 15.929962
## iter 80 value 15.929845
## final value 15.929841
## converged
## # weights: 179
## initial value 118.587377
## iter 10 value 32.762435
## iter 20 value 16.464925
## iter 30 value 13.269555
## iter 40 value 12.109244
## iter 50 value 11.565279
## iter 60 value 11.388141
## iter 70 value 11.359954
## iter 80 value 11.358902
## final value 11.358894
## converged
## # weights: 39
## initial value 101.267206
## iter 10 value 66.116622
## iter 20 value 60.371914
## iter 30 value 60.075559
## iter 40 value 58.924149
## iter 50 value 57.124919
## iter 60 value 57.090024
## iter 70 value 56.411339
## iter 80 value 55.576740
## iter 90 value 55.573734
## iter 100 value 55.291501
## final value 55.291501
## stopped after 100 iterations
## # weights: 109
## initial value 113.510219
## iter 10 value 19.640264
## iter 20 value 14.613993
## iter 30 value 14.115658
## iter 40 value 14.045824
## iter 50 value 14.005903
## iter 60 value 13.979677
## iter 70 value 13.963240
## iter 80 value 13.911021
## iter 90 value 12.170974
## iter 100 value 11.975024
## final value 11.975024
## stopped after 100 iterations
## # weights: 179
## initial value 119.859131
## iter 10 value 20.329641
## iter 20 value 3.312294
## iter 30 value 0.480519
## iter 40 value 0.320360
## iter 50 value 0.263550
## iter 60 value 0.205612
## iter 70 value 0.179905
## iter 80 value 0.152819
## iter 90 value 0.140604
## iter 100 value 0.123132
## final value 0.123132
## stopped after 100 iterations
## # weights: 39
## initial value 101.812966
## iter 10 value 76.264657
## iter 20 value 65.880441
## iter 30 value 63.269674
## iter 40 value 62.040211
## iter 50 value 60.670281
## iter 60 value 58.861189
## iter 70 value 56.117074
## iter 80 value 55.483962
## iter 90 value 53.598026
## iter 100 value 53.285363
## final value 53.285363
## stopped after 100 iterations
## # weights: 109
## initial value 112.752655
## iter 10 value 56.817290
## iter 20 value 47.929750
## iter 30 value 43.441166
## iter 40 value 40.165341
## iter 50 value 39.680246
## iter 60 value 39.069894
## iter 70 value 38.901033
## iter 80 value 38.321157
## iter 90 value 38.206295
## iter 100 value 38.188355
## final value 38.188355
## stopped after 100 iterations
## # weights: 179
## initial value 102.980192
## iter 10 value 28.642033
## iter 20 value 7.009092
## iter 30 value 2.326882
## iter 40 value 1.920285
## iter 50 value 1.910080
## iter 60 value 1.909703
## iter 70 value 1.909602
## iter 80 value 1.909547
## iter 90 value 1.909543
## final value 1.909543
## converged
## # weights: 39
## initial value 108.018927
## iter 10 value 76.035250
## iter 20 value 70.182287
## iter 30 value 67.485350
## iter 40 value 64.979569
## iter 50 value 64.693036
## final value 64.691271
## converged
## # weights: 109
## initial value 110.894363
## iter 10 value 58.984149
## iter 20 value 34.711324
## iter 30 value 18.901614
## iter 40 value 16.341994
## iter 50 value 16.147388
## iter 60 value 16.139168
## iter 70 value 16.139106
## final value 16.139106
## converged
## # weights: 179
## initial value 110.622216
## iter 10 value 45.906126
## iter 20 value 18.942056
## iter 30 value 13.363754
## iter 40 value 12.971982
## iter 50 value 12.732662
## iter 60 value 12.534867
## iter 70 value 12.325362
## iter 80 value 12.154454
## iter 90 value 12.088094
## iter 100 value 11.968421
## final value 11.968421
## stopped after 100 iterations
## # weights: 39
## initial value 123.917267
## iter 10 value 59.369905
## iter 20 value 52.682245
## iter 30 value 51.632216
## iter 40 value 50.580369
## iter 50 value 49.876959
## iter 60 value 49.408913
## iter 70 value 49.205981
## iter 80 value 48.865854
## iter 90 value 47.779097
## iter 100 value 46.084146
## final value 46.084146
## stopped after 100 iterations
## # weights: 109
## initial value 123.525921
## iter 10 value 36.797560
## iter 20 value 14.696041
## iter 30 value 10.193233
## iter 40 value 7.424524
## iter 50 value 6.384276
## iter 60 value 6.321771
## iter 70 value 6.309662
## iter 80 value 6.299374
## iter 90 value 6.289005
## iter 100 value 6.275377
## final value 6.275377
## stopped after 100 iterations
## # weights: 179
## initial value 119.617561
## iter 10 value 10.701262
## iter 20 value 3.743690
## iter 30 value 3.512921
## iter 40 value 3.498513
## iter 50 value 3.473923
## iter 60 value 3.007078
## iter 70 value 0.225592
## iter 80 value 0.177113
## iter 90 value 0.156898
## iter 100 value 0.150699
## final value 0.150699
## stopped after 100 iterations
## # weights: 39
## initial value 101.343288
## iter 10 value 68.317580
## iter 20 value 61.609850
## iter 30 value 60.310510
## iter 40 value 58.972025
## iter 50 value 58.260087
## iter 60 value 56.263434
## iter 70 value 55.072487
## iter 80 value 53.620026
## iter 90 value 47.220028
## iter 100 value 45.520337
## final value 45.520337
## stopped after 100 iterations
## # weights: 109
## initial value 104.532899
## iter 10 value 35.538587
## iter 20 value 21.006508
## iter 30 value 18.979016
## iter 40 value 17.966696
## iter 50 value 17.896767
## iter 60 value 17.884093
## iter 70 value 17.846372
## iter 80 value 17.828210
## iter 90 value 17.816447
## iter 100 value 17.407429
## final value 17.407429
## stopped after 100 iterations
## # weights: 179
## initial value 119.528535
## iter 10 value 31.080294
## iter 20 value 12.244218
## iter 30 value 8.298186
## iter 40 value 6.722362
## iter 50 value 4.072747
## iter 60 value 0.617610
## iter 70 value 0.060200
## iter 80 value 0.008115
## iter 90 value 0.002715
## iter 100 value 0.001207
## final value 0.001207
## stopped after 100 iterations
## # weights: 39
## initial value 103.907486
## iter 10 value 78.223618
## iter 20 value 67.520408
## iter 30 value 61.734353
## iter 40 value 60.454840
## iter 50 value 59.330377
## iter 60 value 59.231196
## final value 59.230475
## converged
## # weights: 109
## initial value 100.840781
## iter 10 value 64.015531
## iter 20 value 36.767201
## iter 30 value 21.924690
## iter 40 value 18.761530
## iter 50 value 18.154767
## iter 60 value 17.188100
## iter 70 value 16.781006
## iter 80 value 16.707305
## iter 90 value 16.705089
## iter 100 value 16.704761
## final value 16.704761
## stopped after 100 iterations
## # weights: 179
## initial value 140.442939
## iter 10 value 65.830523
## iter 20 value 34.889380
## iter 30 value 19.035935
## iter 40 value 13.909923
## iter 50 value 12.793347
## iter 60 value 12.179303
## iter 70 value 12.051163
## iter 80 value 12.044959
## iter 90 value 12.044676
## final value 12.044675
## converged
## # weights: 39
## initial value 104.845205
## iter 10 value 68.660742
## iter 20 value 66.842425
## iter 30 value 66.341481
## iter 40 value 65.556342
## iter 50 value 64.031413
## iter 60 value 63.760341
## iter 70 value 63.007589
## iter 80 value 59.756190
## iter 90 value 58.994077
## iter 100 value 57.312853
## final value 57.312853
## stopped after 100 iterations
## # weights: 109
## initial value 113.894407
## iter 10 value 34.451271
## iter 20 value 21.274297
## iter 30 value 19.963719
## iter 40 value 18.595310
## iter 50 value 18.331691
## iter 60 value 16.835592
## iter 70 value 14.705555
## iter 80 value 13.214716
## iter 90 value 12.962748
## iter 100 value 12.684479
## final value 12.684479
## stopped after 100 iterations
## # weights: 179
## initial value 102.311419
## iter 10 value 28.973385
## iter 20 value 10.704907
## iter 30 value 6.771526
## iter 40 value 6.551950
## iter 50 value 6.507542
## iter 60 value 6.469068
## iter 70 value 6.436237
## iter 80 value 2.923181
## iter 90 value 2.597222
## iter 100 value 2.492442
## final value 2.492442
## stopped after 100 iterations
## # weights: 39
## initial value 108.875625
## iter 10 value 69.392753
## iter 20 value 58.759166
## iter 30 value 53.945300
## iter 40 value 50.712879
## iter 50 value 49.909091
## iter 60 value 49.811994
## iter 70 value 49.799894
## iter 80 value 49.796642
## iter 90 value 49.793880
## iter 100 value 49.793100
## final value 49.793100
## stopped after 100 iterations
## # weights: 109
## initial value 109.152712
## iter 10 value 51.267875
## iter 20 value 27.524327
## iter 30 value 24.488585
## iter 40 value 18.540225
## iter 50 value 17.193689
## iter 60 value 13.627051
## iter 70 value 13.100294
## iter 80 value 13.079647
## iter 90 value 13.055929
## iter 100 value 10.665919
## final value 10.665919
## stopped after 100 iterations
## # weights: 179
## initial value 101.701451
## iter 10 value 13.721538
## iter 20 value 3.396775
## iter 30 value 0.056290
## iter 40 value 0.004440
## iter 50 value 0.000872
## iter 60 value 0.000669
## iter 70 value 0.000135
## final value 0.000098
## converged
## # weights: 39
## initial value 111.190148
## iter 10 value 76.789437
## iter 20 value 61.225167
## iter 30 value 58.633997
## iter 40 value 57.665096
## iter 50 value 57.512190
## iter 60 value 57.506280
## final value 57.506275
## converged
## # weights: 109
## initial value 108.894507
## iter 10 value 53.697986
## iter 20 value 32.308356
## iter 30 value 19.677219
## iter 40 value 17.305521
## iter 50 value 17.273404
## iter 60 value 17.215712
## iter 70 value 17.196846
## final value 17.196768
## converged
## # weights: 179
## initial value 101.106225
## iter 10 value 34.196886
## iter 20 value 23.177652
## iter 30 value 15.454677
## iter 40 value 12.687252
## iter 50 value 12.105685
## iter 60 value 12.067431
## iter 70 value 12.066501
## final value 12.066480
## converged
## # weights: 39
## initial value 109.381752
## iter 10 value 72.643184
## iter 20 value 56.692340
## iter 30 value 55.597493
## iter 40 value 54.487736
## iter 50 value 51.299320
## iter 60 value 50.347533
## iter 70 value 46.795584
## iter 80 value 45.068337
## iter 90 value 45.038756
## iter 100 value 44.683083
## final value 44.683083
## stopped after 100 iterations
## # weights: 109
## initial value 109.943663
## iter 10 value 24.163936
## iter 20 value 17.127179
## iter 30 value 13.224191
## iter 40 value 11.606982
## iter 50 value 11.501432
## iter 60 value 11.244019
## iter 70 value 11.093082
## iter 80 value 10.837152
## iter 90 value 10.760991
## iter 100 value 10.362623
## final value 10.362623
## stopped after 100 iterations
## # weights: 179
## initial value 121.821713
## iter 10 value 32.238282
## iter 20 value 12.867662
## iter 30 value 10.981383
## iter 40 value 10.736350
## iter 50 value 10.689513
## iter 60 value 10.488365
## iter 70 value 8.979011
## iter 80 value 8.293390
## iter 90 value 8.062851
## iter 100 value 7.933613
## final value 7.933613
## stopped after 100 iterations
## # weights: 39
## initial value 99.698071
## iter 10 value 75.609142
## iter 20 value 56.276058
## iter 30 value 50.798012
## iter 40 value 49.171610
## iter 50 value 49.167365
## iter 60 value 47.619038
## iter 70 value 47.592538
## iter 80 value 47.588570
## iter 90 value 47.208237
## iter 100 value 45.883085
## final value 45.883085
## stopped after 100 iterations
## # weights: 109
## initial value 112.228710
## iter 10 value 50.031829
## iter 20 value 25.358031
## iter 30 value 22.199191
## iter 40 value 19.916258
## iter 50 value 19.768160
## iter 60 value 19.628819
## iter 70 value 15.723151
## iter 80 value 15.078645
## iter 90 value 14.580501
## iter 100 value 14.242808
## final value 14.242808
## stopped after 100 iterations
## # weights: 179
## initial value 103.322567
## iter 10 value 42.959707
## iter 20 value 25.348399
## iter 30 value 19.913674
## iter 40 value 17.998580
## iter 50 value 17.643973
## iter 60 value 14.522184
## iter 70 value 14.209321
## iter 80 value 14.144097
## iter 90 value 14.121018
## iter 100 value 14.099880
## final value 14.099880
## stopped after 100 iterations
## # weights: 39
## initial value 102.325985
## iter 10 value 75.244761
## iter 20 value 65.196442
## iter 30 value 59.542262
## iter 40 value 57.397322
## iter 50 value 57.323707
## final value 57.323341
## converged
## # weights: 109
## initial value 114.712539
## iter 10 value 69.088881
## iter 20 value 41.515291
## iter 30 value 25.744990
## iter 40 value 19.023144
## iter 50 value 17.314570
## iter 60 value 16.517578
## iter 70 value 16.355615
## iter 80 value 16.335146
## iter 90 value 16.333710
## final value 16.333697
## converged
## # weights: 179
## initial value 116.393871
## iter 10 value 44.575309
## iter 20 value 22.774751
## iter 30 value 16.367071
## iter 40 value 13.689312
## iter 50 value 12.410021
## iter 60 value 12.303035
## iter 70 value 12.291042
## iter 80 value 12.289979
## iter 90 value 12.289947
## final value 12.289947
## converged
## # weights: 39
## initial value 108.579175
## iter 10 value 70.042935
## iter 20 value 65.978343
## iter 30 value 62.711364
## iter 40 value 62.315902
## iter 50 value 62.028357
## iter 60 value 61.367452
## iter 70 value 58.288791
## iter 80 value 57.072753
## iter 90 value 55.576664
## iter 100 value 55.121945
## final value 55.121945
## stopped after 100 iterations
## # weights: 109
## initial value 110.054935
## iter 10 value 37.239104
## iter 20 value 22.093682
## iter 30 value 21.206249
## iter 40 value 21.051862
## iter 50 value 20.654158
## iter 60 value 19.868018
## iter 70 value 18.161574
## iter 80 value 17.836567
## iter 90 value 17.568132
## iter 100 value 17.427090
## final value 17.427090
## stopped after 100 iterations
## # weights: 179
## initial value 107.010114
## iter 10 value 28.645337
## iter 20 value 15.560719
## iter 30 value 5.357644
## iter 40 value 3.887248
## iter 50 value 3.037402
## iter 60 value 2.455987
## iter 70 value 2.430426
## iter 80 value 2.419593
## iter 90 value 2.403420
## iter 100 value 2.386490
## final value 2.386490
## stopped after 100 iterations
## # weights: 39
## initial value 111.748781
## iter 10 value 82.362494
## iter 20 value 56.491317
## iter 30 value 54.013211
## iter 40 value 52.297342
## iter 50 value 49.649772
## iter 60 value 48.606573
## iter 70 value 48.310407
## iter 80 value 47.988462
## iter 90 value 47.573953
## iter 100 value 46.154498
## final value 46.154498
## stopped after 100 iterations
## # weights: 109
## initial value 100.959771
## iter 10 value 43.658244
## iter 20 value 14.136724
## iter 30 value 8.297663
## iter 40 value 4.641254
## iter 50 value 3.500629
## iter 60 value 3.261167
## iter 70 value 3.171687
## iter 80 value 3.054208
## iter 90 value 3.037085
## iter 100 value 3.019752
## final value 3.019752
## stopped after 100 iterations
## # weights: 179
## initial value 106.894923
## iter 10 value 14.562593
## iter 20 value 3.970936
## iter 30 value 2.851469
## iter 40 value 2.217390
## iter 50 value 0.212774
## iter 60 value 0.010110
## iter 70 value 0.004670
## iter 80 value 0.002390
## iter 90 value 0.001476
## iter 100 value 0.000656
## final value 0.000656
## stopped after 100 iterations
## # weights: 39
## initial value 108.294662
## iter 10 value 71.543210
## iter 20 value 63.169549
## iter 30 value 60.248837
## iter 40 value 59.703745
## iter 50 value 59.093684
## iter 60 value 58.940329
## iter 70 value 58.934163
## final value 58.934057
## converged
## # weights: 109
## initial value 107.229510
## iter 10 value 66.099796
## iter 20 value 39.788575
## iter 30 value 29.661846
## iter 40 value 21.603068
## iter 50 value 19.548814
## iter 60 value 17.492360
## iter 70 value 16.801130
## iter 80 value 16.166626
## iter 90 value 15.990112
## iter 100 value 15.980566
## final value 15.980566
## stopped after 100 iterations
## # weights: 179
## initial value 110.630154
## iter 10 value 35.885232
## iter 20 value 18.442221
## iter 30 value 14.937750
## iter 40 value 13.635050
## iter 50 value 13.154991
## iter 60 value 13.100098
## iter 70 value 13.096890
## iter 80 value 13.096741
## final value 13.096741
## converged
## # weights: 39
## initial value 103.937202
## iter 10 value 56.209423
## iter 20 value 54.194496
## iter 30 value 53.315345
## iter 40 value 53.307815
## iter 50 value 52.983382
## iter 60 value 52.686854
## iter 70 value 52.680336
## iter 80 value 52.647927
## iter 90 value 52.019514
## iter 100 value 51.973899
## final value 51.973899
## stopped after 100 iterations
## # weights: 109
## initial value 147.810753
## iter 10 value 70.247415
## iter 20 value 31.141529
## iter 30 value 16.301170
## iter 40 value 11.499536
## iter 50 value 11.207383
## iter 60 value 9.839510
## iter 70 value 9.783952
## iter 80 value 9.569280
## iter 90 value 9.342981
## iter 100 value 9.077057
## final value 9.077057
## stopped after 100 iterations
## # weights: 179
## initial value 108.066521
## iter 10 value 53.924505
## iter 20 value 23.229846
## iter 30 value 19.255665
## iter 40 value 17.431627
## iter 50 value 17.090793
## iter 60 value 16.640302
## iter 70 value 16.431240
## iter 80 value 13.982969
## iter 90 value 12.105533
## iter 100 value 11.704529
## final value 11.704529
## stopped after 100 iterations
## # weights: 39
## initial value 109.455309
## iter 10 value 66.527842
## iter 20 value 53.468029
## iter 30 value 51.838740
## iter 40 value 51.615120
## iter 50 value 51.578367
## iter 60 value 51.565644
## iter 70 value 51.554775
## iter 80 value 51.551161
## iter 90 value 51.550330
## final value 51.549869
## converged
## # weights: 109
## initial value 108.655688
## iter 10 value 22.018061
## iter 20 value 8.796169
## iter 30 value 7.486149
## iter 40 value 3.371167
## iter 50 value 3.274082
## iter 60 value 2.485679
## iter 70 value 1.530451
## iter 80 value 1.401712
## iter 90 value 1.393707
## iter 100 value 1.389145
## final value 1.389145
## stopped after 100 iterations
## # weights: 179
## initial value 105.730242
## iter 10 value 32.262044
## iter 20 value 16.254203
## iter 30 value 14.906651
## iter 40 value 14.175998
## iter 50 value 10.740408
## iter 60 value 10.615139
## iter 70 value 10.138087
## iter 80 value 8.777355
## iter 90 value 5.569397
## iter 100 value 5.400004
## final value 5.400004
## stopped after 100 iterations
## # weights: 39
## initial value 107.824745
## iter 10 value 79.775394
## iter 20 value 74.062589
## iter 30 value 69.690795
## iter 40 value 68.069902
## iter 50 value 64.858011
## iter 60 value 62.511426
## iter 70 value 61.000542
## iter 80 value 60.223329
## iter 90 value 58.613537
## iter 100 value 58.055390
## final value 58.055390
## stopped after 100 iterations
## # weights: 109
## initial value 114.409881
## iter 10 value 57.420147
## iter 20 value 36.971835
## iter 30 value 22.084784
## iter 40 value 17.102821
## iter 50 value 16.310995
## iter 60 value 15.984661
## iter 70 value 15.940944
## iter 80 value 15.936295
## final value 15.936265
## converged
## # weights: 179
## initial value 112.395813
## iter 10 value 52.080574
## iter 20 value 22.718329
## iter 30 value 15.330558
## iter 40 value 13.498730
## iter 50 value 13.191362
## iter 60 value 12.890545
## iter 70 value 12.800024
## iter 80 value 12.744077
## iter 90 value 12.567801
## iter 100 value 12.551310
## final value 12.551310
## stopped after 100 iterations
## # weights: 39
## initial value 101.427263
## iter 10 value 63.673330
## iter 20 value 60.020893
## iter 30 value 59.354847
## iter 40 value 56.511763
## iter 50 value 56.441490
## iter 60 value 56.399558
## iter 70 value 56.360969
## iter 80 value 56.359130
## iter 90 value 56.350950
## iter 100 value 56.316278
## final value 56.316278
## stopped after 100 iterations
## # weights: 109
## initial value 101.887861
## iter 10 value 26.488203
## iter 20 value 6.914291
## iter 30 value 4.085094
## iter 40 value 3.625470
## iter 50 value 1.756415
## iter 60 value 0.695455
## iter 70 value 0.277673
## iter 80 value 0.271941
## iter 90 value 0.268346
## iter 100 value 0.263656
## final value 0.263656
## stopped after 100 iterations
## # weights: 179
## initial value 95.704309
## iter 10 value 23.349842
## iter 20 value 10.814138
## iter 30 value 8.715380
## iter 40 value 8.624406
## iter 50 value 8.533033
## iter 60 value 8.464953
## iter 70 value 8.437404
## iter 80 value 8.044346
## iter 90 value 6.390358
## iter 100 value 6.193022
## final value 6.193022
## stopped after 100 iterations
## # weights: 39
## initial value 104.296667
## iter 10 value 77.025851
## iter 20 value 62.629354
## iter 30 value 59.263841
## iter 40 value 57.642381
## iter 50 value 57.372222
## iter 60 value 57.333348
## iter 70 value 57.323559
## iter 80 value 57.321180
## iter 90 value 57.321058
## iter 90 value 57.321057
## iter 90 value 57.321057
## final value 57.321057
## converged
## # weights: 109
## initial value 107.296511
## iter 10 value 25.362223
## iter 20 value 14.993591
## iter 30 value 13.715812
## iter 40 value 12.261899
## iter 50 value 11.039153
## iter 60 value 10.227932
## iter 70 value 8.849414
## iter 80 value 7.564418
## iter 90 value 7.488777
## iter 100 value 7.375649
## final value 7.375649
## stopped after 100 iterations
## # weights: 179
## initial value 125.510522
## iter 10 value 45.391295
## iter 20 value 21.017046
## iter 30 value 19.516528
## iter 40 value 15.217035
## iter 50 value 12.013770
## iter 60 value 11.788743
## iter 70 value 11.526496
## iter 80 value 11.361076
## iter 90 value 10.876873
## iter 100 value 7.941496
## final value 7.941496
## stopped after 100 iterations
## # weights: 39
## initial value 99.286956
## iter 10 value 67.852828
## iter 20 value 62.961531
## iter 30 value 62.703163
## iter 40 value 62.682730
## iter 50 value 62.682466
## iter 50 value 62.682465
## iter 50 value 62.682465
## final value 62.682465
## converged
## # weights: 109
## initial value 107.072646
## iter 10 value 50.341556
## iter 20 value 31.801284
## iter 30 value 23.948510
## iter 40 value 18.150609
## iter 50 value 16.220409
## iter 60 value 15.753133
## iter 70 value 15.729246
## iter 80 value 15.728865
## final value 15.728861
## converged
## # weights: 179
## initial value 111.970236
## iter 10 value 34.372965
## iter 20 value 18.064936
## iter 30 value 13.361749
## iter 40 value 12.541324
## iter 50 value 12.154801
## iter 60 value 12.017273
## iter 70 value 12.001270
## iter 80 value 12.000691
## iter 90 value 12.000637
## final value 12.000636
## converged
## # weights: 39
## initial value 103.559374
## iter 10 value 70.010312
## iter 20 value 57.156874
## iter 30 value 53.927386
## iter 40 value 53.572091
## iter 50 value 53.278301
## iter 60 value 53.036635
## iter 70 value 52.943404
## iter 80 value 52.145371
## iter 90 value 52.132646
## iter 100 value 51.302119
## final value 51.302119
## stopped after 100 iterations
## # weights: 109
## initial value 114.608400
## iter 10 value 48.672642
## iter 20 value 24.157843
## iter 30 value 15.504033
## iter 40 value 13.165696
## iter 50 value 11.207666
## iter 60 value 10.470458
## iter 70 value 10.403654
## iter 80 value 10.360537
## iter 90 value 10.239004
## iter 100 value 10.217863
## final value 10.217863
## stopped after 100 iterations
## # weights: 179
## initial value 105.433065
## iter 10 value 28.523213
## iter 20 value 0.728495
## iter 30 value 0.132762
## iter 40 value 0.115776
## iter 50 value 0.103897
## iter 60 value 0.097216
## iter 70 value 0.090305
## iter 80 value 0.083757
## iter 90 value 0.077558
## iter 100 value 0.073939
## final value 0.073939
## stopped after 100 iterations
## # weights: 39
## initial value 100.250715
## iter 10 value 59.500949
## iter 20 value 52.254535
## iter 30 value 48.504939
## iter 40 value 47.572323
## iter 50 value 46.667284
## iter 60 value 46.640032
## iter 70 value 46.616998
## iter 80 value 46.501696
## iter 90 value 46.283256
## iter 100 value 44.891651
## final value 44.891651
## stopped after 100 iterations
## # weights: 109
## initial value 116.473182
## iter 10 value 34.273330
## iter 20 value 24.480114
## iter 30 value 22.813100
## iter 40 value 21.449908
## iter 50 value 20.306050
## iter 60 value 18.465852
## iter 70 value 17.300470
## iter 80 value 13.139466
## iter 90 value 10.840335
## iter 100 value 10.415677
## final value 10.415677
## stopped after 100 iterations
## # weights: 179
## initial value 107.453799
## iter 10 value 16.614371
## iter 20 value 7.011421
## iter 30 value 4.143610
## iter 40 value 4.008715
## iter 50 value 3.960556
## iter 60 value 3.822514
## iter 70 value 3.591313
## iter 80 value 3.539542
## iter 90 value 3.465176
## iter 100 value 3.382262
## final value 3.382262
## stopped after 100 iterations
## # weights: 39
## initial value 103.320865
## iter 10 value 85.209593
## iter 20 value 66.694613
## iter 30 value 61.823827
## iter 40 value 60.877568
## iter 50 value 58.201353
## iter 60 value 56.594871
## iter 70 value 56.391773
## iter 80 value 56.389858
## iter 80 value 56.389858
## iter 80 value 56.389858
## final value 56.389858
## converged
## # weights: 109
## initial value 116.342391
## iter 10 value 56.166226
## iter 20 value 25.635515
## iter 30 value 17.279527
## iter 40 value 16.507618
## iter 50 value 16.292501
## iter 60 value 16.283223
## iter 70 value 16.283039
## final value 16.283037
## converged
## # weights: 179
## initial value 113.283764
## iter 10 value 32.569562
## iter 20 value 20.150417
## iter 30 value 14.426061
## iter 40 value 13.634122
## iter 50 value 13.336895
## iter 60 value 13.030421
## iter 70 value 12.758627
## iter 80 value 12.650265
## iter 90 value 12.607596
## iter 100 value 12.469816
## final value 12.469816
## stopped after 100 iterations
## # weights: 39
## initial value 105.736386
## iter 10 value 63.246322
## iter 20 value 52.470727
## iter 30 value 47.456754
## iter 40 value 46.813960
## iter 50 value 46.603013
## iter 60 value 45.795323
## iter 70 value 45.737514
## iter 80 value 45.709524
## iter 90 value 45.703775
## iter 100 value 45.693047
## final value 45.693047
## stopped after 100 iterations
## # weights: 109
## initial value 93.959235
## iter 10 value 36.519949
## iter 20 value 31.426332
## iter 30 value 30.366494
## iter 40 value 28.880446
## iter 50 value 25.331610
## iter 60 value 19.077266
## iter 70 value 18.280686
## iter 80 value 15.747558
## iter 90 value 15.481766
## iter 100 value 15.079706
## final value 15.079706
## stopped after 100 iterations
## # weights: 179
## initial value 101.788902
## iter 10 value 23.731147
## iter 20 value 10.237795
## iter 30 value 7.849354
## iter 40 value 4.345460
## iter 50 value 2.331632
## iter 60 value 2.168719
## iter 70 value 0.690900
## iter 80 value 0.271838
## iter 90 value 0.249158
## iter 100 value 0.239051
## final value 0.239051
## stopped after 100 iterations
## # weights: 39
## initial value 104.458268
## iter 10 value 70.222783
## iter 20 value 64.231165
## iter 30 value 62.155540
## iter 40 value 60.875710
## iter 50 value 60.857825
## iter 60 value 60.817928
## iter 70 value 60.809706
## iter 80 value 60.806563
## iter 90 value 60.805694
## final value 60.805681
## converged
## # weights: 109
## initial value 103.642164
## iter 10 value 24.228438
## iter 20 value 9.452049
## iter 30 value 3.720791
## iter 40 value 3.301987
## iter 50 value 3.295931
## iter 60 value 3.295837
## final value 3.295837
## converged
## # weights: 179
## initial value 105.704381
## iter 10 value 46.549555
## iter 20 value 20.491579
## iter 30 value 12.353323
## iter 40 value 8.130829
## iter 50 value 7.473297
## iter 60 value 7.132041
## iter 70 value 6.027820
## iter 80 value 3.004632
## iter 90 value 2.800765
## iter 100 value 2.791761
## final value 2.791761
## stopped after 100 iterations
## # weights: 39
## initial value 106.121089
## iter 10 value 74.692239
## iter 20 value 65.606413
## iter 30 value 64.297653
## iter 40 value 61.825284
## iter 50 value 60.633845
## iter 60 value 60.423171
## iter 70 value 60.214923
## iter 80 value 60.105598
## iter 90 value 60.104647
## iter 90 value 60.104647
## iter 90 value 60.104647
## final value 60.104647
## converged
## # weights: 109
## initial value 109.253145
## iter 10 value 57.025161
## iter 20 value 28.112607
## iter 30 value 21.195361
## iter 40 value 18.930384
## iter 50 value 17.568697
## iter 60 value 16.655917
## iter 70 value 15.690680
## iter 80 value 15.524600
## iter 90 value 15.521253
## iter 100 value 15.521152
## final value 15.521152
## stopped after 100 iterations
## # weights: 179
## initial value 138.039064
## iter 10 value 46.598751
## iter 20 value 24.260296
## iter 30 value 18.303595
## iter 40 value 14.067750
## iter 50 value 12.918251
## iter 60 value 12.589741
## iter 70 value 12.541442
## iter 80 value 12.538303
## iter 90 value 12.538141
## final value 12.538140
## converged
## # weights: 39
## initial value 106.356336
## iter 10 value 68.452015
## iter 20 value 65.416227
## iter 30 value 63.947799
## iter 40 value 63.916571
## iter 50 value 61.834987
## iter 60 value 61.669058
## iter 70 value 61.377174
## iter 80 value 61.305702
## iter 90 value 59.349200
## iter 100 value 58.966345
## final value 58.966345
## stopped after 100 iterations
## # weights: 109
## initial value 100.263011
## iter 10 value 66.357584
## iter 20 value 59.432815
## iter 30 value 50.924436
## iter 40 value 46.109702
## iter 50 value 44.394994
## iter 60 value 41.968925
## iter 70 value 40.515520
## iter 80 value 40.426899
## iter 90 value 40.251873
## iter 100 value 31.108218
## final value 31.108218
## stopped after 100 iterations
## # weights: 179
## initial value 109.625086
## iter 10 value 25.068673
## iter 20 value 13.760215
## iter 30 value 10.734845
## iter 40 value 10.147620
## iter 50 value 10.012782
## iter 60 value 7.203749
## iter 70 value 6.831651
## iter 80 value 6.825024
## iter 90 value 6.810372
## iter 100 value 6.727742
## final value 6.727742
## stopped after 100 iterations
## # weights: 39
## initial value 112.072423
## iter 10 value 65.034174
## iter 20 value 51.979473
## iter 30 value 45.653229
## iter 40 value 44.830322
## iter 50 value 43.150441
## iter 60 value 42.872868
## iter 70 value 42.767342
## iter 80 value 40.197044
## iter 90 value 38.393754
## iter 100 value 37.288519
## final value 37.288519
## stopped after 100 iterations
## # weights: 109
## initial value 112.390586
## iter 10 value 39.394985
## iter 20 value 13.491987
## iter 30 value 10.534286
## iter 40 value 10.441899
## iter 50 value 10.383342
## iter 60 value 10.338906
## iter 70 value 10.328905
## iter 80 value 10.313577
## iter 90 value 10.194279
## iter 100 value 10.181215
## final value 10.181215
## stopped after 100 iterations
## # weights: 179
## initial value 124.045733
## iter 10 value 29.267607
## iter 20 value 13.154668
## iter 30 value 6.242953
## iter 40 value 3.889525
## iter 50 value 3.797358
## iter 60 value 3.707217
## iter 70 value 0.017489
## iter 80 value 0.004347
## iter 90 value 0.001443
## iter 100 value 0.000319
## final value 0.000319
## stopped after 100 iterations
## # weights: 39
## initial value 106.405441
## iter 10 value 63.861865
## iter 20 value 58.574330
## iter 30 value 57.998185
## iter 40 value 57.624896
## iter 50 value 57.561700
## iter 60 value 57.551188
## iter 70 value 57.343096
## iter 80 value 57.263553
## final value 57.262028
## converged
## # weights: 109
## initial value 108.289289
## iter 10 value 60.864303
## iter 20 value 34.687795
## iter 30 value 18.546353
## iter 40 value 16.316635
## iter 50 value 16.182487
## iter 60 value 16.128697
## iter 70 value 16.127198
## iter 80 value 16.127105
## final value 16.127102
## converged
## # weights: 179
## initial value 115.851488
## iter 10 value 57.360759
## iter 20 value 22.508297
## iter 30 value 15.326321
## iter 40 value 12.045208
## iter 50 value 11.703389
## iter 60 value 11.673999
## iter 70 value 11.673336
## final value 11.673335
## converged
## # weights: 39
## initial value 108.643417
## iter 10 value 68.845729
## iter 20 value 55.584263
## iter 30 value 49.940049
## iter 40 value 49.388662
## iter 50 value 48.814767
## iter 60 value 48.567796
## iter 70 value 48.290484
## iter 80 value 48.277622
## iter 90 value 48.274766
## iter 100 value 48.272571
## final value 48.272571
## stopped after 100 iterations
## # weights: 109
## initial value 111.395285
## iter 10 value 36.710399
## iter 20 value 22.028561
## iter 30 value 13.310092
## iter 40 value 9.271770
## iter 50 value 6.308165
## iter 60 value 5.595234
## iter 70 value 4.438496
## iter 80 value 2.559946
## iter 90 value 2.471806
## iter 100 value 2.464984
## final value 2.464984
## stopped after 100 iterations
## # weights: 179
## initial value 110.287686
## iter 10 value 23.977702
## iter 20 value 16.030259
## iter 30 value 10.950104
## iter 40 value 10.844199
## iter 50 value 9.432052
## iter 60 value 6.767409
## iter 70 value 5.161374
## iter 80 value 3.610309
## iter 90 value 3.478161
## iter 100 value 3.447109
## final value 3.447109
## stopped after 100 iterations
## # weights: 39
## initial value 104.104121
## iter 10 value 94.632851
## iter 20 value 82.104303
## iter 30 value 71.771913
## iter 40 value 68.250425
## iter 50 value 64.496235
## iter 60 value 56.965377
## iter 70 value 53.989169
## iter 80 value 52.938096
## iter 90 value 52.774512
## iter 100 value 52.755977
## final value 52.755977
## stopped after 100 iterations
## # weights: 109
## initial value 102.660668
## iter 10 value 32.782329
## iter 20 value 29.072581
## iter 30 value 24.595833
## iter 40 value 20.424162
## iter 50 value 15.258664
## iter 60 value 14.370618
## iter 70 value 14.328304
## iter 80 value 14.301886
## iter 90 value 10.218310
## iter 100 value 9.234749
## final value 9.234749
## stopped after 100 iterations
## # weights: 179
## initial value 100.913089
## iter 10 value 23.433515
## iter 20 value 2.742041
## iter 30 value 0.098064
## iter 40 value 0.005778
## iter 50 value 0.001027
## final value 0.000055
## converged
## # weights: 39
## initial value 119.429726
## iter 10 value 77.705077
## iter 20 value 67.193284
## iter 30 value 62.468116
## iter 40 value 58.138880
## iter 50 value 57.973718
## final value 57.973633
## converged
## # weights: 109
## initial value 101.372172
## iter 10 value 48.227120
## iter 20 value 33.567402
## iter 30 value 26.726559
## iter 40 value 20.620389
## iter 50 value 17.158583
## iter 60 value 16.425468
## iter 70 value 16.318073
## iter 80 value 16.311307
## final value 16.311173
## converged
## # weights: 179
## initial value 108.628410
## iter 10 value 62.030342
## iter 20 value 25.043421
## iter 30 value 13.672449
## iter 40 value 12.820465
## iter 50 value 12.536611
## iter 60 value 12.191646
## iter 70 value 12.142232
## iter 80 value 12.138215
## iter 90 value 12.137719
## iter 100 value 12.137587
## final value 12.137587
## stopped after 100 iterations
## # weights: 39
## initial value 116.154045
## iter 10 value 72.039430
## iter 20 value 55.630344
## iter 30 value 51.128362
## iter 40 value 49.381379
## iter 50 value 47.825645
## iter 60 value 47.400458
## iter 70 value 47.143185
## iter 80 value 46.539989
## iter 90 value 46.169071
## iter 100 value 45.233537
## final value 45.233537
## stopped after 100 iterations
## # weights: 109
## initial value 101.979893
## iter 10 value 50.640612
## iter 20 value 15.700287
## iter 30 value 9.068393
## iter 40 value 8.678117
## iter 50 value 8.561720
## iter 60 value 8.531960
## iter 70 value 8.275311
## iter 80 value 8.116934
## iter 90 value 7.840509
## iter 100 value 7.627756
## final value 7.627756
## stopped after 100 iterations
## # weights: 179
## initial value 116.458597
## iter 10 value 38.761667
## iter 20 value 18.181590
## iter 30 value 7.199160
## iter 40 value 4.105810
## iter 50 value 3.607261
## iter 60 value 3.573308
## iter 70 value 3.548038
## iter 80 value 2.955726
## iter 90 value 2.946366
## iter 100 value 2.927672
## final value 2.927672
## stopped after 100 iterations
## # weights: 39
## initial value 102.902944
## iter 10 value 95.117534
## iter 20 value 77.992387
## iter 30 value 59.882523
## iter 40 value 51.135631
## iter 50 value 50.046115
## iter 60 value 50.042183
## iter 70 value 50.041889
## iter 70 value 50.041889
## iter 70 value 50.041889
## final value 50.041889
## converged
## # weights: 109
## initial value 115.267663
## iter 10 value 58.436888
## iter 20 value 47.112767
## iter 30 value 41.952219
## iter 40 value 39.953379
## iter 50 value 38.487842
## iter 60 value 37.859635
## iter 70 value 35.776093
## iter 80 value 33.528924
## iter 90 value 33.365070
## iter 100 value 32.374284
## final value 32.374284
## stopped after 100 iterations
## # weights: 179
## initial value 100.684060
## iter 10 value 20.204483
## iter 20 value 10.006904
## iter 30 value 6.390664
## iter 40 value 5.958816
## iter 50 value 4.871625
## iter 60 value 4.310074
## iter 70 value 3.993121
## iter 80 value 3.893802
## iter 90 value 3.053489
## iter 100 value 2.850379
## final value 2.850379
## stopped after 100 iterations
## # weights: 39
## initial value 112.928870
## iter 10 value 71.810066
## iter 20 value 60.569788
## iter 30 value 58.130220
## iter 40 value 57.674019
## iter 50 value 56.638793
## iter 60 value 55.707852
## iter 70 value 55.699390
## iter 80 value 55.682629
## iter 90 value 55.531367
## iter 100 value 55.501614
## final value 55.501614
## stopped after 100 iterations
## # weights: 109
## initial value 132.454427
## iter 10 value 75.339895
## iter 20 value 51.015371
## iter 30 value 42.506734
## iter 40 value 31.643265
## iter 50 value 20.287321
## iter 60 value 17.622962
## iter 70 value 17.331452
## iter 80 value 17.257635
## iter 90 value 17.194175
## iter 100 value 17.139716
## final value 17.139716
## stopped after 100 iterations
## # weights: 179
## initial value 109.431568
## iter 10 value 28.271596
## iter 20 value 14.086766
## iter 30 value 12.745537
## iter 40 value 12.577017
## iter 50 value 12.399960
## iter 60 value 12.338756
## iter 70 value 12.333465
## final value 12.333432
## converged
## # weights: 39
## initial value 100.913687
## iter 10 value 68.321926
## iter 20 value 62.361350
## iter 30 value 58.081019
## iter 40 value 57.146854
## iter 50 value 56.980564
## iter 60 value 55.674238
## iter 70 value 54.617325
## iter 80 value 54.531249
## iter 90 value 54.403833
## iter 100 value 54.393019
## final value 54.393019
## stopped after 100 iterations
## # weights: 109
## initial value 99.323085
## iter 10 value 43.400727
## iter 20 value 29.610046
## iter 30 value 28.215557
## iter 40 value 25.187745
## iter 50 value 19.445128
## iter 60 value 16.395059
## iter 70 value 14.051096
## iter 80 value 13.170538
## iter 90 value 12.611976
## iter 100 value 11.583229
## final value 11.583229
## stopped after 100 iterations
## # weights: 179
## initial value 121.662153
## iter 10 value 33.659989
## iter 20 value 5.332921
## iter 30 value 0.551199
## iter 40 value 0.139551
## iter 50 value 0.130606
## iter 60 value 0.125440
## iter 70 value 0.115584
## iter 80 value 0.109488
## iter 90 value 0.103554
## iter 100 value 0.100060
## final value 0.100060
## stopped after 100 iterations
## # weights: 39
## initial value 103.035516
## iter 10 value 67.202549
## iter 20 value 58.454661
## iter 30 value 57.810290
## iter 40 value 56.269648
## iter 50 value 53.340897
## iter 60 value 52.388565
## iter 70 value 49.889017
## iter 80 value 49.094245
## iter 90 value 43.408274
## iter 100 value 42.153114
## final value 42.153114
## stopped after 100 iterations
## # weights: 109
## initial value 98.606029
## iter 10 value 43.828124
## iter 20 value 25.446274
## iter 30 value 23.327318
## iter 40 value 22.973549
## iter 50 value 22.861778
## iter 60 value 22.585147
## iter 70 value 22.362169
## iter 80 value 22.339553
## iter 90 value 22.329936
## iter 100 value 22.311773
## final value 22.311773
## stopped after 100 iterations
## # weights: 179
## initial value 116.721945
## iter 10 value 21.158818
## iter 20 value 11.175851
## iter 30 value 10.507747
## iter 40 value 10.179334
## iter 50 value 10.032282
## iter 60 value 6.514140
## iter 70 value 5.639228
## iter 80 value 5.567743
## iter 90 value 5.268058
## iter 100 value 5.216528
## final value 5.216528
## stopped after 100 iterations
## # weights: 39
## initial value 102.506497
## iter 10 value 81.787259
## iter 20 value 64.685533
## iter 30 value 60.413218
## iter 40 value 58.380602
## iter 50 value 58.173716
## iter 60 value 58.165468
## iter 70 value 57.826258
## iter 80 value 57.746568
## iter 90 value 57.744660
## final value 57.744659
## converged
## # weights: 109
## initial value 126.906785
## iter 10 value 75.492805
## iter 20 value 46.356830
## iter 30 value 23.509959
## iter 40 value 18.760807
## iter 50 value 17.803642
## iter 60 value 17.494283
## iter 70 value 17.189130
## iter 80 value 17.119594
## iter 90 value 17.119079
## final value 17.119077
## converged
## # weights: 179
## initial value 125.422441
## iter 10 value 49.803856
## iter 20 value 30.976175
## iter 30 value 19.400579
## iter 40 value 15.109142
## iter 50 value 12.935283
## iter 60 value 12.372935
## iter 70 value 12.210302
## iter 80 value 12.150268
## iter 90 value 12.138439
## iter 100 value 12.138117
## final value 12.138117
## stopped after 100 iterations
## # weights: 39
## initial value 105.568778
## iter 10 value 65.287381
## iter 20 value 61.661921
## iter 30 value 59.188873
## iter 40 value 58.762466
## iter 50 value 57.473034
## iter 60 value 56.486467
## iter 70 value 56.288190
## iter 80 value 56.213820
## iter 90 value 54.391989
## iter 100 value 52.768477
## final value 52.768477
## stopped after 100 iterations
## # weights: 109
## initial value 111.525584
## iter 10 value 46.274228
## iter 20 value 25.018974
## iter 30 value 22.193350
## iter 40 value 18.413932
## iter 50 value 18.067061
## iter 60 value 18.047277
## iter 70 value 18.036309
## iter 80 value 15.719904
## iter 90 value 14.513767
## iter 100 value 14.427055
## final value 14.427055
## stopped after 100 iterations
## # weights: 179
## initial value 142.483354
## iter 10 value 80.870178
## iter 20 value 70.300416
## iter 30 value 67.024735
## iter 40 value 54.827191
## iter 50 value 40.970342
## iter 60 value 40.772189
## iter 70 value 40.761977
## iter 80 value 37.473880
## iter 90 value 37.412983
## iter 100 value 36.054658
## final value 36.054658
## stopped after 100 iterations
## # weights: 39
## initial value 106.375245
## iter 10 value 61.477491
## iter 20 value 51.579410
## iter 30 value 46.683733
## iter 40 value 46.577219
## iter 50 value 46.576626
## final value 46.576594
## converged
## # weights: 109
## initial value 109.682889
## iter 10 value 53.976210
## iter 20 value 25.163669
## iter 30 value 23.454847
## iter 40 value 22.771648
## iter 50 value 22.558459
## iter 60 value 21.628530
## iter 70 value 21.590289
## iter 80 value 21.587335
## iter 90 value 20.701509
## iter 100 value 20.587487
## final value 20.587487
## stopped after 100 iterations
## # weights: 179
## initial value 126.896091
## iter 10 value 26.618382
## iter 20 value 17.197097
## iter 30 value 14.435782
## iter 40 value 12.924168
## iter 50 value 12.652587
## iter 60 value 9.980644
## iter 70 value 8.921548
## iter 80 value 7.109506
## iter 90 value 5.476058
## iter 100 value 4.769754
## final value 4.769754
## stopped after 100 iterations
## # weights: 39
## initial value 135.096055
## iter 10 value 76.609294
## iter 20 value 65.883392
## iter 30 value 59.641980
## iter 40 value 58.993143
## iter 50 value 58.771664
## iter 60 value 58.393686
## iter 70 value 57.922598
## iter 80 value 57.906495
## final value 57.906494
## converged
## # weights: 109
## initial value 117.493313
## iter 10 value 66.028895
## iter 20 value 34.052163
## iter 30 value 21.372445
## iter 40 value 18.469352
## iter 50 value 17.637266
## iter 60 value 17.389107
## iter 70 value 17.362444
## iter 80 value 17.352768
## iter 90 value 17.352390
## iter 100 value 17.352227
## final value 17.352227
## stopped after 100 iterations
## # weights: 179
## initial value 141.970467
## iter 10 value 42.564692
## iter 20 value 23.248382
## iter 30 value 15.557352
## iter 40 value 13.332413
## iter 50 value 12.920563
## iter 60 value 12.735156
## iter 70 value 12.526042
## iter 80 value 12.494029
## iter 90 value 12.482295
## iter 100 value 12.480921
## final value 12.480921
## stopped after 100 iterations
## # weights: 39
## initial value 104.043870
## iter 10 value 85.293057
## iter 20 value 75.115480
## iter 30 value 69.764047
## iter 40 value 67.298602
## iter 50 value 66.038954
## iter 60 value 61.247599
## iter 70 value 58.184208
## iter 80 value 51.591317
## iter 90 value 48.286815
## iter 100 value 47.542761
## final value 47.542761
## stopped after 100 iterations
## # weights: 109
## initial value 107.342746
## iter 10 value 41.088781
## iter 20 value 19.871015
## iter 30 value 19.145624
## iter 40 value 19.110781
## iter 50 value 19.083913
## iter 60 value 19.058351
## iter 70 value 19.019856
## iter 80 value 18.981652
## iter 90 value 17.728232
## iter 100 value 16.666380
## final value 16.666380
## stopped after 100 iterations
## # weights: 179
## initial value 99.458461
## iter 10 value 22.889092
## iter 20 value 3.951857
## iter 30 value 0.800651
## iter 40 value 0.211433
## iter 50 value 0.184478
## iter 60 value 0.178162
## iter 70 value 0.162257
## iter 80 value 0.148402
## iter 90 value 0.139573
## iter 100 value 0.128653
## final value 0.128653
## stopped after 100 iterations
## # weights: 39
## initial value 116.998611
## iter 10 value 66.983932
## iter 20 value 61.799025
## iter 30 value 61.003114
## iter 40 value 59.892893
## iter 50 value 57.741088
## iter 60 value 57.686713
## iter 70 value 57.570713
## iter 80 value 57.433716
## iter 90 value 53.141803
## iter 100 value 52.253071
## final value 52.253071
## stopped after 100 iterations
## # weights: 109
## initial value 117.964956
## iter 10 value 34.259599
## iter 20 value 16.807223
## iter 30 value 9.643818
## iter 40 value 8.987121
## iter 50 value 8.309009
## iter 60 value 8.194304
## iter 70 value 8.053956
## iter 80 value 8.025009
## iter 90 value 8.020143
## iter 100 value 8.015874
## final value 8.015874
## stopped after 100 iterations
## # weights: 179
## initial value 119.662556
## iter 10 value 17.294015
## iter 20 value 2.497720
## iter 30 value 1.914160
## iter 40 value 1.909786
## iter 50 value 1.909551
## final value 1.909548
## converged
## # weights: 39
## initial value 105.830669
## iter 10 value 69.251233
## iter 20 value 65.584326
## iter 30 value 62.058325
## iter 40 value 58.951676
## iter 50 value 58.196204
## iter 60 value 57.888591
## iter 70 value 57.696740
## iter 80 value 57.659527
## final value 57.659311
## converged
## # weights: 109
## initial value 96.087951
## iter 10 value 36.653478
## iter 20 value 25.450749
## iter 30 value 20.252368
## iter 40 value 19.021345
## iter 50 value 18.586706
## iter 60 value 18.134710
## iter 70 value 17.807973
## iter 80 value 17.553251
## iter 90 value 17.194338
## iter 100 value 16.794765
## final value 16.794765
## stopped after 100 iterations
## # weights: 179
## initial value 128.862280
## iter 10 value 51.729388
## iter 20 value 29.209093
## iter 30 value 18.841753
## iter 40 value 14.122869
## iter 50 value 13.008250
## iter 60 value 12.489594
## iter 70 value 12.336610
## iter 80 value 12.333355
## iter 90 value 12.333115
## final value 12.333106
## converged
## # weights: 39
## initial value 104.183800
## iter 10 value 63.136261
## iter 20 value 57.728559
## iter 30 value 55.069735
## iter 40 value 53.686989
## iter 50 value 53.514559
## iter 60 value 53.438879
## iter 70 value 52.184845
## iter 80 value 51.868603
## iter 90 value 51.630860
## iter 100 value 51.609710
## final value 51.609710
## stopped after 100 iterations
## # weights: 109
## initial value 110.258659
## iter 10 value 37.486723
## iter 20 value 15.789121
## iter 30 value 8.936070
## iter 40 value 4.311441
## iter 50 value 3.604217
## iter 60 value 3.529040
## iter 70 value 3.421301
## iter 80 value 3.329895
## iter 90 value 3.247734
## iter 100 value 3.207721
## final value 3.207721
## stopped after 100 iterations
## # weights: 179
## initial value 118.203115
## iter 10 value 24.695646
## iter 20 value 15.585488
## iter 30 value 12.046722
## iter 40 value 10.907912
## iter 50 value 9.025753
## iter 60 value 8.922034
## iter 70 value 8.245400
## iter 80 value 7.672546
## iter 90 value 7.563322
## iter 100 value 7.436171
## final value 7.436171
## stopped after 100 iterations
## # weights: 39
## initial value 105.082330
## iter 10 value 89.250841
## iter 20 value 55.053026
## iter 30 value 54.537456
## iter 40 value 54.536669
## final value 54.536667
## converged
## # weights: 109
## initial value 103.218517
## iter 10 value 25.488058
## iter 20 value 12.656920
## iter 30 value 11.459115
## iter 40 value 4.611962
## iter 50 value 4.515423
## iter 60 value 4.039090
## iter 70 value 3.852664
## iter 80 value 3.808005
## iter 90 value 3.385339
## iter 100 value 3.375242
## final value 3.375242
## stopped after 100 iterations
## # weights: 179
## initial value 103.608427
## iter 10 value 26.686248
## iter 20 value 5.952020
## iter 30 value 2.505683
## iter 40 value 1.452198
## iter 50 value 1.420127
## iter 60 value 1.409059
## iter 70 value 1.397804
## iter 80 value 1.392694
## iter 90 value 1.390771
## iter 100 value 1.387596
## final value 1.387596
## stopped after 100 iterations
## # weights: 39
## initial value 105.100112
## iter 10 value 79.815206
## iter 20 value 76.877191
## iter 30 value 72.711150
## iter 40 value 68.563403
## iter 50 value 65.585533
## iter 60 value 65.327311
## iter 70 value 64.694536
## iter 80 value 64.670858
## final value 64.670794
## converged
## # weights: 109
## initial value 107.917549
## iter 10 value 42.563395
## iter 20 value 24.146781
## iter 30 value 17.251261
## iter 40 value 16.814284
## iter 50 value 16.779971
## iter 60 value 16.778538
## iter 70 value 16.774396
## iter 80 value 16.624766
## iter 90 value 16.470744
## iter 100 value 16.441429
## final value 16.441429
## stopped after 100 iterations
## # weights: 179
## initial value 117.939171
## iter 10 value 29.243039
## iter 20 value 15.473215
## iter 30 value 13.352784
## iter 40 value 12.871967
## iter 50 value 12.801351
## iter 60 value 12.779367
## iter 70 value 12.734912
## iter 80 value 12.733841
## final value 12.733839
## converged
## # weights: 39
## initial value 103.095073
## iter 10 value 64.405363
## iter 20 value 62.998348
## iter 30 value 62.165910
## iter 40 value 61.457724
## iter 50 value 53.992489
## iter 60 value 49.790352
## iter 70 value 48.980479
## iter 80 value 48.645495
## iter 90 value 48.125494
## iter 100 value 47.291032
## final value 47.291032
## stopped after 100 iterations
## # weights: 109
## initial value 107.104233
## iter 10 value 45.795587
## iter 20 value 27.037214
## iter 30 value 25.324404
## iter 40 value 18.295890
## iter 50 value 17.929887
## iter 60 value 15.633515
## iter 70 value 15.614014
## iter 80 value 15.539602
## iter 90 value 15.103116
## iter 100 value 15.091108
## final value 15.091108
## stopped after 100 iterations
## # weights: 179
## initial value 118.595524
## iter 10 value 9.228820
## iter 20 value 2.031334
## iter 30 value 1.542707
## iter 40 value 1.520213
## iter 50 value 1.512758
## iter 60 value 1.505597
## iter 70 value 1.497186
## iter 80 value 1.493245
## iter 90 value 1.473857
## iter 100 value 0.155951
## final value 0.155951
## stopped after 100 iterations
## # weights: 39
## initial value 102.403787
## iter 10 value 71.052825
## iter 20 value 59.423164
## iter 30 value 59.009155
## iter 40 value 57.553718
## iter 50 value 57.306853
## iter 60 value 56.834363
## iter 70 value 55.727993
## iter 80 value 55.399706
## iter 90 value 55.161329
## iter 100 value 51.264628
## final value 51.264628
## stopped after 100 iterations
## # weights: 109
## initial value 113.248935
## iter 10 value 30.123739
## iter 20 value 19.919473
## iter 30 value 14.442042
## iter 40 value 13.044798
## iter 50 value 12.703637
## iter 60 value 12.224476
## iter 70 value 11.806999
## iter 80 value 11.469849
## iter 90 value 11.337181
## iter 100 value 11.161889
## final value 11.161889
## stopped after 100 iterations
## # weights: 179
## initial value 110.001748
## iter 10 value 52.973058
## iter 20 value 24.542659
## iter 30 value 20.327671
## iter 40 value 15.764694
## iter 50 value 12.767671
## iter 60 value 11.057671
## iter 70 value 10.757761
## iter 80 value 9.868986
## iter 90 value 9.761236
## iter 100 value 8.169763
## final value 8.169763
## stopped after 100 iterations
## # weights: 39
## initial value 107.747973
## iter 10 value 79.727757
## iter 20 value 66.926899
## iter 30 value 61.571105
## iter 40 value 59.733705
## iter 50 value 58.496820
## iter 60 value 57.872763
## iter 70 value 57.590517
## iter 80 value 57.414411
## iter 90 value 57.407494
## final value 57.407487
## converged
## # weights: 109
## initial value 115.641531
## iter 10 value 50.739236
## iter 20 value 28.280328
## iter 30 value 20.508272
## iter 40 value 18.338220
## iter 50 value 16.100876
## iter 60 value 15.716135
## iter 70 value 15.687606
## iter 80 value 15.686209
## iter 90 value 15.686146
## final value 15.686145
## converged
## # weights: 179
## initial value 121.731627
## iter 10 value 54.072596
## iter 20 value 22.979033
## iter 30 value 14.531366
## iter 40 value 12.499999
## iter 50 value 12.058427
## iter 60 value 12.048304
## iter 70 value 12.047228
## iter 80 value 12.030373
## iter 90 value 11.834911
## iter 100 value 11.637256
## final value 11.637256
## stopped after 100 iterations
## # weights: 39
## initial value 103.681866
## iter 10 value 59.304041
## iter 20 value 57.324948
## iter 30 value 56.343407
## iter 40 value 55.373041
## iter 50 value 55.365170
## iter 60 value 54.616535
## iter 70 value 53.684371
## iter 80 value 53.680127
## iter 90 value 53.209106
## iter 100 value 52.734290
## final value 52.734290
## stopped after 100 iterations
## # weights: 109
## initial value 106.838757
## iter 10 value 51.870309
## iter 20 value 47.948553
## iter 30 value 45.257253
## iter 40 value 27.247696
## iter 50 value 21.938418
## iter 60 value 21.333957
## iter 70 value 20.602551
## iter 80 value 19.347027
## iter 90 value 18.797962
## iter 100 value 18.166324
## final value 18.166324
## stopped after 100 iterations
## # weights: 179
## initial value 105.674945
## iter 10 value 22.027455
## iter 20 value 6.654449
## iter 30 value 5.701856
## iter 40 value 5.564341
## iter 50 value 5.509315
## iter 60 value 5.363430
## iter 70 value 4.969073
## iter 80 value 2.069842
## iter 90 value 1.811697
## iter 100 value 1.731294
## final value 1.731294
## stopped after 100 iterations
## # weights: 39
## initial value 112.869840
## iter 10 value 72.876987
## iter 20 value 61.547193
## iter 30 value 60.758307
## iter 40 value 59.498706
## iter 50 value 58.710610
## iter 60 value 57.083188
## iter 70 value 55.638628
## iter 80 value 54.573927
## iter 90 value 54.446970
## iter 100 value 53.641197
## final value 53.641197
## stopped after 100 iterations
## # weights: 109
## initial value 126.900732
## iter 10 value 57.223975
## iter 20 value 36.585155
## iter 30 value 21.758451
## iter 40 value 10.124779
## iter 50 value 9.390504
## iter 60 value 9.263628
## iter 70 value 9.154817
## iter 80 value 9.111984
## iter 90 value 8.968643
## iter 100 value 8.951311
## final value 8.951311
## stopped after 100 iterations
## # weights: 179
## initial value 104.283855
## iter 10 value 20.758754
## iter 20 value 11.884268
## iter 30 value 11.178848
## iter 40 value 10.693612
## iter 50 value 10.635022
## iter 60 value 10.495912
## iter 70 value 10.433276
## iter 80 value 10.222896
## iter 90 value 10.208746
## iter 100 value 10.102354
## final value 10.102354
## stopped after 100 iterations
## # weights: 39
## initial value 106.704691
## iter 10 value 77.931973
## iter 20 value 65.162515
## iter 30 value 61.752405
## iter 40 value 59.647890
## iter 50 value 58.439908
## iter 60 value 58.134947
## iter 70 value 57.999751
## iter 80 value 57.484663
## iter 90 value 57.465099
## final value 57.465084
## converged
## # weights: 109
## initial value 101.281788
## iter 10 value 52.614419
## iter 20 value 28.933868
## iter 30 value 18.307748
## iter 40 value 17.591372
## iter 50 value 17.427833
## iter 60 value 17.423486
## iter 70 value 17.423402
## final value 17.423398
## converged
## # weights: 179
## initial value 112.107421
## iter 10 value 40.687887
## iter 20 value 19.017303
## iter 30 value 14.149128
## iter 40 value 12.888287
## iter 50 value 12.503109
## iter 60 value 12.410647
## iter 70 value 12.407067
## iter 80 value 12.406993
## final value 12.406989
## converged
## # weights: 39
## initial value 102.966885
## iter 10 value 63.608194
## iter 20 value 56.201441
## iter 30 value 52.428983
## iter 40 value 51.147780
## iter 50 value 51.134441
## iter 60 value 51.122064
## iter 70 value 51.076005
## iter 80 value 51.051720
## iter 90 value 51.042610
## iter 100 value 51.035130
## final value 51.035130
## stopped after 100 iterations
## # weights: 109
## initial value 113.610656
## iter 10 value 46.467633
## iter 20 value 35.301876
## iter 30 value 30.066421
## iter 40 value 29.601292
## iter 50 value 27.812802
## iter 60 value 24.038850
## iter 70 value 19.216655
## iter 80 value 17.452832
## iter 90 value 15.998938
## iter 100 value 15.301963
## final value 15.301963
## stopped after 100 iterations
## # weights: 179
## initial value 93.727114
## iter 10 value 17.982797
## iter 20 value 5.874062
## iter 30 value 4.320749
## iter 40 value 4.294416
## iter 50 value 4.274965
## iter 60 value 4.253659
## iter 70 value 3.822158
## iter 80 value 3.416225
## iter 90 value 3.400938
## iter 100 value 1.657367
## final value 1.657367
## stopped after 100 iterations
## # weights: 39
## initial value 111.071494
## iter 10 value 81.917978
## iter 20 value 66.752529
## iter 30 value 63.269597
## iter 40 value 58.737235
## iter 50 value 55.649019
## iter 60 value 54.526292
## iter 70 value 49.324847
## iter 80 value 47.430320
## iter 90 value 46.798140
## iter 100 value 45.420131
## final value 45.420131
## stopped after 100 iterations
## # weights: 109
## initial value 107.089943
## iter 10 value 55.234740
## iter 20 value 22.389060
## iter 30 value 20.367074
## iter 40 value 19.609503
## iter 50 value 18.495072
## iter 60 value 17.262961
## iter 70 value 17.040185
## iter 80 value 16.865915
## iter 90 value 16.466185
## iter 100 value 15.883832
## final value 15.883832
## stopped after 100 iterations
## # weights: 179
## initial value 103.652883
## iter 10 value 24.167727
## iter 20 value 9.953042
## iter 30 value 8.492294
## iter 40 value 7.065063
## iter 50 value 5.199502
## iter 60 value 5.189567
## iter 70 value 5.152705
## iter 80 value 5.133026
## iter 90 value 5.127129
## iter 100 value 5.126972
## final value 5.126972
## stopped after 100 iterations
## # weights: 39
## initial value 107.242423
## iter 10 value 69.668594
## iter 20 value 62.957453
## iter 30 value 60.293402
## iter 40 value 60.078461
## iter 50 value 59.938452
## iter 60 value 59.513597
## iter 70 value 59.165025
## iter 80 value 59.112118
## final value 59.112044
## converged
## # weights: 109
## initial value 115.412115
## iter 10 value 73.212100
## iter 20 value 49.088261
## iter 30 value 23.155727
## iter 40 value 18.003186
## iter 50 value 16.879399
## iter 60 value 16.798870
## iter 70 value 16.781864
## iter 80 value 16.742496
## iter 90 value 16.738341
## iter 100 value 16.738237
## final value 16.738237
## stopped after 100 iterations
## # weights: 179
## initial value 121.281352
## iter 10 value 35.370770
## iter 20 value 21.488152
## iter 30 value 16.066384
## iter 40 value 13.597310
## iter 50 value 12.605599
## iter 60 value 12.278221
## iter 70 value 12.251660
## iter 80 value 12.251127
## iter 90 value 12.251106
## final value 12.251106
## converged
## # weights: 39
## initial value 104.544298
## iter 10 value 64.526224
## iter 20 value 50.388044
## iter 30 value 40.914239
## iter 40 value 38.582250
## iter 50 value 37.377818
## iter 60 value 36.922249
## iter 70 value 36.221746
## iter 80 value 36.176123
## iter 90 value 36.064625
## iter 100 value 35.393734
## final value 35.393734
## stopped after 100 iterations
## # weights: 109
## initial value 111.341716
## iter 10 value 41.008508
## iter 20 value 19.711727
## iter 30 value 12.263611
## iter 40 value 6.314696
## iter 50 value 5.384760
## iter 60 value 5.206651
## iter 70 value 4.913414
## iter 80 value 4.753253
## iter 90 value 4.732967
## iter 100 value 4.709362
## final value 4.709362
## stopped after 100 iterations
## # weights: 179
## initial value 121.416140
## iter 10 value 17.285821
## iter 20 value 7.404485
## iter 30 value 5.593378
## iter 40 value 5.517382
## iter 50 value 5.495217
## iter 60 value 5.325118
## iter 70 value 5.272660
## iter 80 value 5.152961
## iter 90 value 5.139881
## iter 100 value 5.121958
## final value 5.121958
## stopped after 100 iterations
## # weights: 39
## initial value 101.176678
## iter 10 value 64.942117
## iter 20 value 60.189055
## iter 30 value 59.479518
## iter 40 value 59.208528
## iter 50 value 59.171348
## iter 60 value 59.166573
## iter 70 value 59.164137
## iter 80 value 59.163955
## iter 80 value 59.163954
## final value 59.163954
## converged
## # weights: 109
## initial value 104.413824
## iter 10 value 42.854235
## iter 20 value 19.182867
## iter 30 value 9.740918
## iter 40 value 6.772048
## iter 50 value 6.278772
## iter 60 value 6.272093
## iter 70 value 6.270951
## iter 80 value 5.937595
## iter 90 value 5.417071
## iter 100 value 5.408201
## final value 5.408201
## stopped after 100 iterations
## # weights: 179
## initial value 127.217693
## iter 10 value 22.643510
## iter 20 value 6.696243
## iter 30 value 2.929428
## iter 40 value 2.774617
## iter 50 value 2.772779
## iter 60 value 2.772590
## iter 60 value 2.772590
## iter 60 value 2.772590
## final value 2.772590
## converged
## # weights: 39
## initial value 103.130121
## iter 10 value 73.534905
## iter 20 value 65.080629
## iter 30 value 60.105258
## iter 40 value 59.068942
## iter 50 value 58.569938
## iter 60 value 58.541653
## final value 58.541651
## converged
## # weights: 109
## initial value 110.493544
## iter 10 value 55.135778
## iter 20 value 25.915758
## iter 30 value 19.037411
## iter 40 value 16.209721
## iter 50 value 15.725766
## iter 60 value 15.703111
## iter 70 value 15.701788
## iter 80 value 15.701748
## iter 80 value 15.701748
## iter 80 value 15.701748
## final value 15.701748
## converged
## # weights: 179
## initial value 106.481457
## iter 10 value 43.837386
## iter 20 value 25.044892
## iter 30 value 16.600008
## iter 40 value 12.913796
## iter 50 value 12.234388
## iter 60 value 12.095502
## iter 70 value 12.084205
## iter 80 value 12.083223
## iter 90 value 12.083188
## final value 12.083188
## converged
## # weights: 39
## initial value 111.716414
## iter 10 value 60.088700
## iter 20 value 45.960138
## iter 30 value 36.906685
## iter 40 value 36.697115
## iter 50 value 36.695805
## iter 60 value 36.690875
## iter 70 value 36.675566
## iter 80 value 36.485801
## iter 90 value 36.339357
## iter 100 value 36.130272
## final value 36.130272
## stopped after 100 iterations
## # weights: 109
## initial value 115.061469
## iter 10 value 42.919828
## iter 20 value 7.646608
## iter 30 value 1.946823
## iter 40 value 1.557959
## iter 50 value 1.545121
## iter 60 value 1.535179
## iter 70 value 1.529208
## iter 80 value 1.517631
## iter 90 value 1.505618
## iter 100 value 1.499120
## final value 1.499120
## stopped after 100 iterations
## # weights: 179
## initial value 110.006198
## iter 10 value 22.072325
## iter 20 value 5.538083
## iter 30 value 4.070544
## iter 40 value 4.054390
## iter 50 value 4.030999
## iter 60 value 4.013053
## iter 70 value 4.005433
## iter 80 value 4.000324
## iter 90 value 2.715954
## iter 100 value 1.505921
## final value 1.505921
## stopped after 100 iterations
## # weights: 39
## initial value 100.615696
## iter 10 value 70.908875
## iter 20 value 62.751346
## iter 30 value 62.623699
## iter 40 value 62.138530
## iter 50 value 61.813073
## iter 60 value 61.674940
## iter 70 value 61.658481
## iter 80 value 61.655601
## final value 61.655160
## converged
## # weights: 109
## initial value 98.295046
## iter 10 value 28.535016
## iter 20 value 16.738147
## iter 30 value 10.518791
## iter 40 value 9.740156
## iter 50 value 9.713299
## iter 60 value 9.712616
## iter 70 value 9.712360
## iter 80 value 9.712324
## iter 80 value 9.712324
## iter 80 value 9.712324
## final value 9.712324
## converged
## # weights: 179
## initial value 141.452737
## iter 10 value 53.685731
## iter 20 value 32.433635
## iter 30 value 10.732553
## iter 40 value 6.465266
## iter 50 value 6.420500
## iter 60 value 6.419666
## iter 60 value 6.419666
## final value 6.419661
## converged
## # weights: 39
## initial value 120.204565
## iter 10 value 77.903940
## iter 20 value 69.317038
## iter 30 value 63.987192
## iter 40 value 57.967337
## iter 50 value 56.541860
## iter 60 value 56.213978
## iter 70 value 56.191838
## final value 56.191761
## converged
## # weights: 109
## initial value 98.917610
## iter 10 value 52.279871
## iter 20 value 25.291209
## iter 30 value 19.246674
## iter 40 value 17.625722
## iter 50 value 16.587821
## iter 60 value 15.734542
## iter 70 value 15.531990
## iter 80 value 14.800242
## iter 90 value 14.701720
## iter 100 value 14.701363
## final value 14.701363
## stopped after 100 iterations
## # weights: 179
## initial value 101.512058
## iter 10 value 27.811450
## iter 20 value 16.304479
## iter 30 value 14.120042
## iter 40 value 12.382638
## iter 50 value 11.901144
## iter 60 value 11.770871
## iter 70 value 11.753453
## iter 80 value 11.712876
## iter 90 value 11.644096
## iter 100 value 11.642399
## final value 11.642399
## stopped after 100 iterations
## # weights: 39
## initial value 108.666967
## iter 10 value 57.735899
## iter 20 value 55.543966
## iter 30 value 52.538535
## iter 40 value 51.607325
## iter 50 value 50.763429
## iter 60 value 50.319685
## iter 70 value 49.824164
## iter 80 value 49.102181
## iter 90 value 48.040041
## iter 100 value 46.626782
## final value 46.626782
## stopped after 100 iterations
## # weights: 109
## initial value 108.529147
## iter 10 value 38.569147
## iter 20 value 22.200431
## iter 30 value 19.489820
## iter 40 value 18.875881
## iter 50 value 18.553506
## iter 60 value 18.253276
## iter 70 value 17.943551
## iter 80 value 17.543093
## iter 90 value 16.221792
## iter 100 value 12.620674
## final value 12.620674
## stopped after 100 iterations
## # weights: 179
## initial value 111.860137
## iter 10 value 23.551875
## iter 20 value 15.385911
## iter 30 value 12.002694
## iter 40 value 9.867249
## iter 50 value 8.245347
## iter 60 value 6.836543
## iter 70 value 5.994746
## iter 80 value 5.893996
## iter 90 value 5.886723
## iter 100 value 5.879668
## final value 5.879668
## stopped after 100 iterations
## # weights: 39
## initial value 125.381831
## iter 10 value 59.430396
## iter 20 value 54.499055
## iter 30 value 53.870191
## iter 40 value 50.254981
## iter 50 value 48.537299
## iter 60 value 45.884463
## iter 70 value 44.671552
## iter 80 value 43.035189
## iter 90 value 41.526255
## iter 100 value 40.408186
## final value 40.408186
## stopped after 100 iterations
## # weights: 109
## initial value 99.277951
## iter 10 value 30.409227
## iter 20 value 22.269757
## iter 30 value 21.168598
## iter 40 value 20.948904
## iter 50 value 20.228458
## iter 60 value 19.645825
## iter 70 value 19.171710
## iter 80 value 13.522211
## iter 90 value 12.446779
## iter 100 value 12.217019
## final value 12.217019
## stopped after 100 iterations
## # weights: 179
## initial value 117.691961
## iter 10 value 24.356242
## iter 20 value 4.174138
## iter 30 value 2.542639
## iter 40 value 1.987332
## iter 50 value 1.921060
## iter 60 value 1.396960
## iter 70 value 1.213953
## iter 80 value 0.039781
## iter 90 value 0.017388
## iter 100 value 0.010123
## final value 0.010123
## stopped after 100 iterations
## # weights: 39
## initial value 103.734468
## iter 10 value 82.434762
## iter 20 value 76.419268
## iter 30 value 74.340681
## iter 40 value 66.736398
## iter 50 value 60.171751
## iter 60 value 59.261387
## iter 70 value 59.160117
## iter 80 value 59.153974
## iter 90 value 59.149758
## final value 59.149371
## converged
## # weights: 109
## initial value 117.087569
## iter 10 value 54.563837
## iter 20 value 29.708833
## iter 30 value 20.466822
## iter 40 value 16.553459
## iter 50 value 16.027797
## iter 60 value 15.908921
## iter 70 value 15.904742
## final value 15.904707
## converged
## # weights: 179
## initial value 133.017919
## iter 10 value 51.055854
## iter 20 value 26.825720
## iter 30 value 17.235838
## iter 40 value 14.839835
## iter 50 value 13.504617
## iter 60 value 13.143227
## iter 70 value 13.070481
## iter 80 value 13.065182
## iter 90 value 13.064800
## final value 13.064796
## converged
## # weights: 39
## initial value 111.560022
## iter 10 value 61.382274
## iter 20 value 59.144678
## iter 30 value 57.434150
## iter 40 value 56.551149
## iter 50 value 56.196886
## iter 60 value 56.191776
## iter 70 value 56.178702
## iter 80 value 56.173533
## iter 90 value 56.171754
## iter 100 value 53.370590
## final value 53.370590
## stopped after 100 iterations
## # weights: 109
## initial value 105.656784
## iter 10 value 29.579034
## iter 20 value 17.022579
## iter 30 value 11.998677
## iter 40 value 11.604007
## iter 50 value 10.964693
## iter 60 value 10.483192
## iter 70 value 10.173165
## iter 80 value 9.247171
## iter 90 value 5.546509
## iter 100 value 3.332295
## final value 3.332295
## stopped after 100 iterations
## # weights: 179
## initial value 130.620688
## iter 10 value 18.945212
## iter 20 value 5.478681
## iter 30 value 0.324884
## iter 40 value 0.264313
## iter 50 value 0.248027
## iter 60 value 0.226594
## iter 70 value 0.199760
## iter 80 value 0.187477
## iter 90 value 0.173920
## iter 100 value 0.152350
## final value 0.152350
## stopped after 100 iterations
## # weights: 39
## initial value 132.634666
## iter 10 value 66.432181
## iter 20 value 59.978997
## iter 30 value 57.184030
## iter 40 value 55.396428
## iter 50 value 55.266661
## iter 60 value 54.554297
## iter 70 value 54.413852
## iter 80 value 51.479047
## iter 90 value 50.612051
## iter 100 value 50.152796
## final value 50.152796
## stopped after 100 iterations
## # weights: 109
## initial value 112.325222
## iter 10 value 45.065819
## iter 20 value 41.828079
## iter 30 value 40.897232
## iter 40 value 38.441074
## iter 50 value 37.514908
## iter 60 value 37.398699
## iter 70 value 37.293897
## iter 80 value 36.482215
## iter 90 value 35.473921
## iter 100 value 34.896229
## final value 34.896229
## stopped after 100 iterations
## # weights: 179
## initial value 117.848681
## iter 10 value 38.381277
## iter 20 value 12.627887
## iter 30 value 11.528537
## iter 40 value 3.844474
## iter 50 value 1.475254
## iter 60 value 1.407491
## iter 70 value 1.392987
## iter 80 value 1.390012
## iter 90 value 1.387098
## iter 100 value 1.386890
## final value 1.386890
## stopped after 100 iterations
## # weights: 39
## initial value 101.476665
## iter 10 value 65.977246
## iter 20 value 58.949671
## iter 30 value 57.805392
## iter 40 value 57.360984
## iter 50 value 57.352099
## iter 60 value 57.186211
## iter 70 value 57.150682
## final value 57.150674
## converged
## # weights: 109
## initial value 113.489608
## iter 10 value 45.925595
## iter 20 value 28.292826
## iter 30 value 21.638044
## iter 40 value 16.787323
## iter 50 value 16.024587
## iter 60 value 16.015320
## final value 16.015268
## converged
## # weights: 179
## initial value 121.361312
## iter 10 value 47.459172
## iter 20 value 18.961341
## iter 30 value 13.396851
## iter 40 value 12.608863
## iter 50 value 12.338122
## iter 60 value 12.238220
## iter 70 value 12.212839
## iter 80 value 12.156551
## iter 90 value 12.149355
## iter 100 value 12.133294
## final value 12.133294
## stopped after 100 iterations
## # weights: 39
## initial value 109.661937
## iter 10 value 77.565882
## iter 20 value 62.761738
## iter 30 value 59.549675
## iter 40 value 59.533504
## iter 50 value 59.529589
## iter 60 value 59.521517
## iter 70 value 59.514869
## iter 80 value 59.508767
## iter 90 value 59.507936
## iter 100 value 59.503015
## final value 59.503015
## stopped after 100 iterations
## # weights: 109
## initial value 101.397478
## iter 10 value 23.124885
## iter 20 value 16.422129
## iter 30 value 11.303697
## iter 40 value 8.890215
## iter 50 value 7.882660
## iter 60 value 7.707716
## iter 70 value 7.360925
## iter 80 value 7.023340
## iter 90 value 7.009456
## iter 100 value 6.996354
## final value 6.996354
## stopped after 100 iterations
## # weights: 179
## initial value 119.905231
## iter 10 value 23.534014
## iter 20 value 7.603594
## iter 30 value 6.242536
## iter 40 value 6.148501
## iter 50 value 6.118216
## iter 60 value 6.016889
## iter 70 value 5.903810
## iter 80 value 5.801545
## iter 90 value 5.774141
## iter 100 value 5.740878
## final value 5.740878
## stopped after 100 iterations
## # weights: 39
## initial value 104.365260
## iter 10 value 63.392815
## iter 20 value 54.505861
## iter 30 value 53.204722
## iter 40 value 53.054473
## iter 50 value 53.047051
## iter 60 value 53.046884
## iter 70 value 53.046021
## iter 80 value 52.591981
## iter 90 value 51.817189
## iter 100 value 51.225781
## final value 51.225781
## stopped after 100 iterations
## # weights: 109
## initial value 102.003652
## iter 10 value 65.106586
## iter 20 value 59.726281
## iter 30 value 37.507349
## iter 40 value 34.601319
## iter 50 value 34.200664
## iter 60 value 34.079395
## iter 70 value 33.895410
## iter 80 value 33.792463
## iter 90 value 33.688125
## iter 100 value 33.018704
## final value 33.018704
## stopped after 100 iterations
## # weights: 179
## initial value 104.296831
## iter 10 value 24.826703
## iter 20 value 9.417795
## iter 30 value 3.751953
## iter 40 value 1.430299
## iter 50 value 1.390445
## iter 60 value 1.386499
## iter 70 value 1.386319
## final value 1.386309
## converged
## # weights: 39
## initial value 112.249720
## iter 10 value 82.377741
## iter 20 value 65.225815
## iter 30 value 61.082804
## iter 40 value 60.321273
## iter 50 value 59.766642
## iter 60 value 59.460017
## iter 70 value 59.275278
## iter 80 value 59.269600
## final value 59.269597
## converged
## # weights: 109
## initial value 98.417431
## iter 10 value 46.763227
## iter 20 value 27.148624
## iter 30 value 19.624904
## iter 40 value 17.051812
## iter 50 value 16.866195
## iter 60 value 16.850877
## iter 70 value 16.816337
## iter 80 value 16.815395
## iter 80 value 16.815395
## iter 80 value 16.815395
## final value 16.815395
## converged
## # weights: 179
## initial value 109.204876
## iter 10 value 31.394152
## iter 20 value 18.959073
## iter 30 value 13.412071
## iter 40 value 12.603279
## iter 50 value 12.299192
## iter 60 value 12.246947
## iter 70 value 12.235987
## iter 80 value 12.235343
## final value 12.235338
## converged
## # weights: 39
## initial value 102.465655
## iter 10 value 67.804516
## iter 20 value 59.106721
## iter 30 value 58.414318
## iter 40 value 58.092958
## iter 50 value 58.033481
## iter 60 value 57.403094
## iter 70 value 57.350592
## iter 80 value 56.990757
## iter 90 value 56.584704
## iter 100 value 56.164187
## final value 56.164187
## stopped after 100 iterations
## # weights: 109
## initial value 107.082272
## iter 10 value 44.120119
## iter 20 value 32.415559
## iter 30 value 24.105333
## iter 40 value 22.052746
## iter 50 value 21.362392
## iter 60 value 21.258328
## iter 70 value 20.312056
## iter 80 value 19.718686
## iter 90 value 18.647001
## iter 100 value 18.182505
## final value 18.182505
## stopped after 100 iterations
## # weights: 179
## initial value 100.450887
## iter 10 value 27.959970
## iter 20 value 9.859642
## iter 30 value 7.553618
## iter 40 value 7.406723
## iter 50 value 5.801168
## iter 60 value 5.518196
## iter 70 value 2.178912
## iter 80 value 2.063924
## iter 90 value 0.191537
## iter 100 value 0.152051
## final value 0.152051
## stopped after 100 iterations
## # weights: 39
## initial value 100.013615
## iter 10 value 72.787279
## iter 20 value 63.070146
## iter 30 value 59.665286
## iter 40 value 53.093217
## iter 50 value 48.811438
## iter 60 value 47.399302
## iter 70 value 47.354607
## iter 80 value 47.353988
## iter 90 value 47.353938
## iter 100 value 47.353815
## final value 47.353815
## stopped after 100 iterations
## # weights: 109
## initial value 102.732668
## iter 10 value 25.540886
## iter 20 value 17.464998
## iter 30 value 14.543454
## iter 40 value 12.149225
## iter 50 value 12.079525
## iter 60 value 12.017061
## iter 70 value 11.977074
## iter 80 value 11.957155
## iter 90 value 11.394754
## iter 100 value 8.201888
## final value 8.201888
## stopped after 100 iterations
## # weights: 179
## initial value 101.654034
## iter 10 value 24.646667
## iter 20 value 12.122785
## iter 30 value 7.874649
## iter 40 value 4.283369
## iter 50 value 2.048326
## iter 60 value 1.576807
## iter 70 value 1.409501
## iter 80 value 1.392652
## iter 90 value 1.389672
## iter 100 value 1.388002
## final value 1.388002
## stopped after 100 iterations
## # weights: 39
## initial value 107.346384
## iter 10 value 79.371858
## iter 20 value 67.848347
## iter 30 value 61.103750
## iter 40 value 58.935888
## iter 50 value 58.887047
## iter 60 value 58.807020
## iter 70 value 58.636979
## iter 80 value 58.630905
## final value 58.630798
## converged
## # weights: 109
## initial value 105.446120
## iter 10 value 52.118723
## iter 20 value 34.099337
## iter 30 value 25.456442
## iter 40 value 19.012625
## iter 50 value 16.690466
## iter 60 value 16.368163
## iter 70 value 16.290256
## iter 80 value 16.275549
## iter 90 value 16.275493
## iter 90 value 16.275492
## iter 90 value 16.275492
## final value 16.275492
## converged
## # weights: 179
## initial value 113.423118
## iter 10 value 39.074852
## iter 20 value 20.573736
## iter 30 value 15.891826
## iter 40 value 15.059643
## iter 50 value 14.109002
## iter 60 value 13.536565
## iter 70 value 13.480627
## iter 80 value 13.388071
## iter 90 value 13.367145
## iter 100 value 13.367023
## final value 13.367023
## stopped after 100 iterations
## # weights: 39
## initial value 99.011014
## iter 10 value 67.534745
## iter 20 value 54.632320
## iter 30 value 42.330114
## iter 40 value 38.200964
## iter 50 value 37.347592
## iter 60 value 37.089102
## iter 70 value 37.064313
## iter 80 value 37.050889
## iter 90 value 37.041796
## iter 100 value 37.034643
## final value 37.034643
## stopped after 100 iterations
## # weights: 109
## initial value 124.201139
## iter 10 value 63.366108
## iter 20 value 36.294349
## iter 30 value 29.860287
## iter 40 value 23.996252
## iter 50 value 21.145933
## iter 60 value 20.633198
## iter 70 value 18.287112
## iter 80 value 15.414280
## iter 90 value 14.508768
## iter 100 value 13.970275
## final value 13.970275
## stopped after 100 iterations
## # weights: 179
## initial value 110.248930
## iter 10 value 29.247711
## iter 20 value 6.927468
## iter 30 value 3.938158
## iter 40 value 0.780701
## iter 50 value 0.610786
## iter 60 value 0.560039
## iter 70 value 0.494278
## iter 80 value 0.448504
## iter 90 value 0.416301
## iter 100 value 0.378287
## final value 0.378287
## stopped after 100 iterations
## # weights: 39
## initial value 101.112182
## iter 10 value 69.163739
## iter 20 value 64.403387
## iter 30 value 63.118340
## iter 40 value 61.958518
## iter 50 value 59.887504
## iter 60 value 57.873813
## iter 70 value 56.733137
## iter 80 value 55.832158
## iter 90 value 55.199328
## iter 100 value 55.116929
## final value 55.116929
## stopped after 100 iterations
## # weights: 109
## initial value 114.873206
## iter 10 value 48.086287
## iter 20 value 25.003243
## iter 30 value 8.093000
## iter 40 value 7.262368
## iter 50 value 7.232719
## iter 60 value 7.194943
## iter 70 value 7.179123
## iter 80 value 7.170579
## iter 90 value 7.169971
## iter 100 value 7.168742
## final value 7.168742
## stopped after 100 iterations
## # weights: 179
## initial value 112.545934
## iter 10 value 25.580178
## iter 20 value 4.309494
## iter 30 value 0.679900
## iter 40 value 0.020290
## iter 50 value 0.001471
## final value 0.000067
## converged
## # weights: 39
## initial value 100.628925
## iter 10 value 84.191757
## iter 20 value 64.405217
## iter 30 value 60.069852
## iter 40 value 59.206315
## iter 50 value 58.696651
## iter 60 value 58.386407
## iter 70 value 58.077664
## iter 80 value 57.963999
## iter 90 value 57.746399
## iter 100 value 57.656410
## final value 57.656410
## stopped after 100 iterations
## # weights: 109
## initial value 102.712319
## iter 10 value 57.271088
## iter 20 value 32.577705
## iter 30 value 21.161682
## iter 40 value 19.056265
## iter 50 value 18.537374
## iter 60 value 17.898509
## iter 70 value 17.072957
## iter 80 value 16.342303
## iter 90 value 15.862463
## iter 100 value 15.845592
## final value 15.845592
## stopped after 100 iterations
## # weights: 179
## initial value 144.385534
## iter 10 value 65.453418
## iter 20 value 30.082407
## iter 30 value 19.865822
## iter 40 value 14.870588
## iter 50 value 13.052061
## iter 60 value 12.660293
## iter 70 value 12.633034
## iter 80 value 12.629903
## iter 90 value 12.629522
## iter 100 value 12.629374
## final value 12.629374
## stopped after 100 iterations
## # weights: 39
## initial value 119.248779
## iter 10 value 81.651733
## iter 20 value 64.698356
## iter 30 value 61.569624
## iter 40 value 60.096654
## iter 50 value 58.277674
## iter 60 value 57.227460
## iter 70 value 55.309194
## iter 80 value 54.030771
## iter 90 value 52.164866
## iter 100 value 51.956583
## final value 51.956583
## stopped after 100 iterations
## # weights: 109
## initial value 97.029798
## iter 10 value 29.309002
## iter 20 value 20.818587
## iter 30 value 15.780611
## iter 40 value 9.409804
## iter 50 value 7.279159
## iter 60 value 6.842010
## iter 70 value 3.094760
## iter 80 value 2.022031
## iter 90 value 1.337496
## iter 100 value 0.373052
## final value 0.373052
## stopped after 100 iterations
## # weights: 179
## initial value 112.177852
## iter 10 value 23.286637
## iter 20 value 8.737599
## iter 30 value 4.669394
## iter 40 value 4.021378
## iter 50 value 3.949291
## iter 60 value 0.281557
## iter 70 value 0.167801
## iter 80 value 0.154585
## iter 90 value 0.146025
## iter 100 value 0.139925
## final value 0.139925
## stopped after 100 iterations
## # weights: 39
## initial value 118.519049
## iter 10 value 61.501998
## iter 20 value 55.210430
## iter 30 value 52.539765
## iter 40 value 48.348727
## iter 50 value 46.697931
## iter 60 value 46.550618
## iter 70 value 46.414120
## iter 80 value 46.292525
## iter 90 value 46.289972
## iter 100 value 46.153273
## final value 46.153273
## stopped after 100 iterations
## # weights: 109
## initial value 108.280884
## iter 10 value 60.686264
## iter 20 value 25.029261
## iter 30 value 14.629653
## iter 40 value 10.722690
## iter 50 value 6.349505
## iter 60 value 5.936056
## iter 70 value 5.929903
## final value 5.929868
## converged
## # weights: 179
## initial value 108.176733
## iter 10 value 27.574741
## iter 20 value 14.631122
## iter 30 value 8.158751
## iter 40 value 6.909229
## iter 50 value 6.300621
## iter 60 value 5.723647
## iter 70 value 5.421031
## iter 80 value 5.369927
## iter 90 value 5.331224
## iter 100 value 3.722920
## final value 3.722920
## stopped after 100 iterations
## # weights: 39
## initial value 115.232971
## iter 10 value 69.758693
## iter 20 value 64.761897
## iter 30 value 61.181257
## iter 40 value 59.399406
## iter 50 value 58.995393
## iter 60 value 57.818407
## iter 70 value 57.360522
## iter 80 value 57.345151
## final value 57.345145
## converged
## # weights: 109
## initial value 108.911437
## iter 10 value 61.028015
## iter 20 value 35.752664
## iter 30 value 21.891819
## iter 40 value 18.258882
## iter 50 value 16.936475
## iter 60 value 16.436822
## iter 70 value 16.271295
## iter 80 value 16.261078
## iter 90 value 16.251588
## iter 100 value 16.251409
## final value 16.251409
## stopped after 100 iterations
## # weights: 179
## initial value 119.041526
## iter 10 value 49.206682
## iter 20 value 22.421851
## iter 30 value 13.948967
## iter 40 value 12.814434
## iter 50 value 12.341171
## iter 60 value 12.268220
## iter 70 value 12.229556
## iter 80 value 12.197191
## iter 90 value 12.174379
## iter 100 value 12.157878
## final value 12.157878
## stopped after 100 iterations
## # weights: 39
## initial value 97.215681
## iter 10 value 70.697269
## iter 20 value 64.682495
## iter 30 value 64.362166
## iter 40 value 64.350125
## iter 50 value 64.314574
## iter 60 value 64.068797
## iter 70 value 63.927673
## iter 80 value 62.692212
## iter 90 value 60.181767
## iter 100 value 59.632597
## final value 59.632597
## stopped after 100 iterations
## # weights: 109
## initial value 106.258342
## iter 10 value 39.982607
## iter 20 value 13.739876
## iter 30 value 9.679355
## iter 40 value 9.108804
## iter 50 value 8.798725
## iter 60 value 8.674735
## iter 70 value 8.637160
## iter 80 value 8.183904
## iter 90 value 8.073294
## iter 100 value 8.061025
## final value 8.061025
## stopped after 100 iterations
## # weights: 179
## initial value 119.048084
## iter 10 value 27.697311
## iter 20 value 9.322368
## iter 30 value 3.989614
## iter 40 value 2.970221
## iter 50 value 2.897294
## iter 60 value 2.888727
## iter 70 value 2.880494
## iter 80 value 2.870165
## iter 90 value 2.860032
## iter 100 value 2.852935
## final value 2.852935
## stopped after 100 iterations
## # weights: 39
## initial value 111.149016
## iter 10 value 59.249574
## iter 20 value 56.799885
## iter 30 value 55.768447
## iter 40 value 53.866826
## iter 50 value 53.638321
## iter 60 value 52.619167
## iter 70 value 52.562417
## iter 80 value 51.711950
## iter 90 value 50.193433
## iter 100 value 50.115124
## final value 50.115124
## stopped after 100 iterations
## # weights: 109
## initial value 120.478818
## iter 10 value 67.833659
## iter 20 value 41.302787
## iter 30 value 21.157923
## iter 40 value 16.469425
## iter 50 value 12.546110
## iter 60 value 12.485211
## iter 70 value 12.476795
## iter 80 value 10.650705
## iter 90 value 10.364717
## iter 100 value 10.364237
## final value 10.364237
## stopped after 100 iterations
## # weights: 179
## initial value 114.454145
## iter 10 value 16.939293
## iter 20 value 4.300866
## iter 30 value 3.082681
## iter 40 value 2.905748
## iter 50 value 2.885898
## iter 60 value 2.877612
## iter 70 value 2.875402
## iter 80 value 2.704870
## iter 90 value 1.940258
## iter 100 value 1.911908
## final value 1.911908
## stopped after 100 iterations
## # weights: 39
## initial value 100.387194
## iter 10 value 70.217411
## iter 20 value 60.755226
## iter 30 value 58.459146
## iter 40 value 58.395455
## iter 50 value 58.393270
## iter 60 value 58.392859
## iter 70 value 58.384166
## iter 80 value 58.139540
## iter 90 value 57.940458
## iter 100 value 57.734953
## final value 57.734953
## stopped after 100 iterations
## # weights: 109
## initial value 100.236716
## iter 10 value 57.664173
## iter 20 value 40.208650
## iter 30 value 34.571060
## iter 40 value 31.554027
## iter 50 value 30.273822
## iter 60 value 23.253135
## iter 70 value 18.033571
## iter 80 value 16.815295
## iter 90 value 16.413356
## iter 100 value 16.372940
## final value 16.372940
## stopped after 100 iterations
## # weights: 179
## initial value 105.599145
## iter 10 value 35.419437
## iter 20 value 21.509726
## iter 30 value 13.699850
## iter 40 value 12.722746
## iter 50 value 12.410336
## iter 60 value 12.237326
## iter 70 value 12.208050
## iter 80 value 12.205368
## iter 90 value 12.205307
## final value 12.205307
## converged
## # weights: 39
## initial value 104.021162
## iter 10 value 67.735671
## iter 20 value 54.975114
## iter 30 value 51.190925
## iter 40 value 49.416031
## iter 50 value 46.811496
## iter 60 value 46.023158
## iter 70 value 46.014531
## iter 80 value 46.012534
## iter 90 value 46.011122
## iter 100 value 46.009466
## final value 46.009466
## stopped after 100 iterations
## # weights: 109
## initial value 103.029809
## iter 10 value 37.001603
## iter 20 value 26.585757
## iter 30 value 25.544211
## iter 40 value 22.505811
## iter 50 value 21.856030
## iter 60 value 21.576618
## iter 70 value 21.317179
## iter 80 value 20.569577
## iter 90 value 19.385355
## iter 100 value 18.749509
## final value 18.749509
## stopped after 100 iterations
## # weights: 179
## initial value 121.796246
## iter 10 value 16.741709
## iter 20 value 7.182538
## iter 30 value 6.069735
## iter 40 value 5.904295
## iter 50 value 3.454545
## iter 60 value 3.197128
## iter 70 value 3.174952
## iter 80 value 3.168557
## iter 90 value 3.162084
## iter 100 value 2.452021
## final value 2.452021
## stopped after 100 iterations
## # weights: 39
## initial value 99.835018
## iter 10 value 64.139714
## iter 20 value 59.136419
## iter 30 value 58.351445
## iter 40 value 56.894510
## iter 50 value 56.145387
## iter 60 value 55.849723
## iter 70 value 55.343415
## iter 80 value 54.062454
## iter 90 value 48.676833
## iter 100 value 47.973814
## final value 47.973814
## stopped after 100 iterations
## # weights: 109
## initial value 103.989234
## iter 10 value 25.531325
## iter 20 value 14.618317
## iter 30 value 11.585792
## iter 40 value 10.808705
## iter 50 value 10.686602
## iter 60 value 10.681725
## iter 70 value 10.681480
## iter 80 value 10.681342
## final value 10.681337
## converged
## # weights: 179
## initial value 103.651941
## iter 10 value 20.573670
## iter 20 value 7.258001
## iter 30 value 6.427372
## iter 40 value 5.321907
## iter 50 value 5.038972
## iter 60 value 3.342662
## iter 70 value 1.424823
## iter 80 value 1.398727
## iter 90 value 1.389793
## iter 100 value 1.387581
## final value 1.387581
## stopped after 100 iterations
## # weights: 39
## initial value 118.101022
## iter 10 value 74.528816
## iter 20 value 66.073868
## iter 30 value 63.539119
## iter 40 value 62.167036
## iter 50 value 59.977259
## iter 60 value 58.359114
## iter 70 value 57.095552
## iter 80 value 57.085091
## final value 57.085072
## converged
## # weights: 109
## initial value 128.313116
## iter 10 value 45.443998
## iter 20 value 30.975705
## iter 30 value 22.556465
## iter 40 value 19.120438
## iter 50 value 16.689159
## iter 60 value 16.033363
## iter 70 value 15.971582
## iter 80 value 15.970670
## final value 15.970669
## converged
## # weights: 179
## initial value 125.929231
## iter 10 value 35.058444
## iter 20 value 22.100637
## iter 30 value 15.814520
## iter 40 value 13.818183
## iter 50 value 12.628284
## iter 60 value 12.358238
## iter 70 value 12.307827
## iter 80 value 12.306101
## final value 12.306096
## converged
## # weights: 39
## initial value 99.326396
## iter 10 value 82.216544
## iter 20 value 64.939521
## iter 30 value 63.908722
## iter 40 value 62.587678
## iter 50 value 60.093624
## iter 60 value 59.953191
## iter 70 value 58.671882
## iter 80 value 57.937884
## iter 90 value 57.678145
## iter 100 value 57.165342
## final value 57.165342
## stopped after 100 iterations
## # weights: 109
## initial value 105.851003
## iter 10 value 48.300781
## iter 20 value 29.207361
## iter 30 value 23.583297
## iter 40 value 16.083091
## iter 50 value 10.835469
## iter 60 value 7.270159
## iter 70 value 6.811351
## iter 80 value 1.549172
## iter 90 value 0.449533
## iter 100 value 0.344423
## final value 0.344423
## stopped after 100 iterations
## # weights: 179
## initial value 111.053173
## iter 10 value 28.161055
## iter 20 value 15.727787
## iter 30 value 12.524916
## iter 40 value 8.329882
## iter 50 value 5.749663
## iter 60 value 5.138311
## iter 70 value 1.509500
## iter 80 value 0.524562
## iter 90 value 0.165988
## iter 100 value 0.159039
## final value 0.159039
## stopped after 100 iterations
## # weights: 39
## initial value 97.771886
## iter 10 value 71.802876
## iter 20 value 48.792172
## iter 30 value 42.675303
## iter 40 value 41.464500
## iter 50 value 41.022904
## iter 60 value 40.919860
## iter 70 value 36.891822
## iter 80 value 36.586095
## iter 90 value 36.346641
## iter 100 value 36.169138
## final value 36.169138
## stopped after 100 iterations
## # weights: 109
## initial value 96.353102
## iter 10 value 41.644640
## iter 20 value 22.653758
## iter 30 value 20.355682
## iter 40 value 19.091525
## iter 50 value 14.506707
## iter 60 value 13.435666
## iter 70 value 12.357248
## iter 80 value 12.193715
## iter 90 value 12.143197
## iter 100 value 9.857857
## final value 9.857857
## stopped after 100 iterations
## # weights: 179
## initial value 130.062787
## iter 10 value 24.911806
## iter 20 value 0.176277
## iter 30 value 0.002512
## iter 40 value 0.000823
## iter 50 value 0.000186
## final value 0.000099
## converged
## # weights: 39
## initial value 111.723343
## iter 10 value 89.499088
## iter 20 value 70.174704
## iter 30 value 59.073672
## iter 40 value 57.701416
## iter 50 value 56.914247
## iter 60 value 56.825340
## iter 70 value 56.822534
## iter 80 value 56.555028
## iter 90 value 56.527851
## final value 56.527762
## converged
## # weights: 109
## initial value 123.109595
## iter 10 value 67.009656
## iter 20 value 46.738516
## iter 30 value 30.910552
## iter 40 value 22.322239
## iter 50 value 17.473177
## iter 60 value 16.721120
## iter 70 value 16.681969
## iter 80 value 16.679929
## final value 16.679916
## converged
## # weights: 179
## initial value 105.469595
## iter 10 value 50.454871
## iter 20 value 26.553241
## iter 30 value 15.000018
## iter 40 value 12.786934
## iter 50 value 12.580253
## iter 60 value 12.548610
## iter 70 value 12.519697
## iter 80 value 12.498324
## iter 90 value 12.494423
## iter 100 value 12.493171
## final value 12.493171
## stopped after 100 iterations
## # weights: 39
## initial value 102.425049
## iter 10 value 80.706777
## iter 20 value 75.749902
## iter 30 value 75.720012
## iter 40 value 75.717355
## iter 50 value 75.706752
## iter 60 value 73.783920
## iter 70 value 73.745947
## iter 80 value 70.673906
## iter 90 value 68.185829
## iter 100 value 67.531625
## final value 67.531625
## stopped after 100 iterations
## # weights: 109
## initial value 105.762316
## iter 10 value 17.454759
## iter 20 value 13.722771
## iter 30 value 13.084458
## iter 40 value 8.736795
## iter 50 value 8.163552
## iter 60 value 8.144101
## iter 70 value 8.120489
## iter 80 value 8.105749
## iter 90 value 8.093289
## iter 100 value 8.082560
## final value 8.082560
## stopped after 100 iterations
## # weights: 179
## initial value 105.951267
## iter 10 value 20.900748
## iter 20 value 4.647714
## iter 30 value 3.801717
## iter 40 value 2.498879
## iter 50 value 2.473813
## iter 60 value 2.129036
## iter 70 value 2.074201
## iter 80 value 1.601013
## iter 90 value 1.586641
## iter 100 value 1.571057
## final value 1.571057
## stopped after 100 iterations
## # weights: 39
## initial value 102.364770
## iter 10 value 66.870295
## iter 20 value 57.882774
## iter 30 value 53.996184
## iter 40 value 53.496147
## iter 50 value 53.003330
## iter 60 value 52.238964
## iter 70 value 52.114358
## iter 80 value 50.692797
## iter 90 value 50.051595
## iter 100 value 49.947281
## final value 49.947281
## stopped after 100 iterations
## # weights: 109
## initial value 103.884128
## iter 10 value 52.189002
## iter 20 value 29.958777
## iter 30 value 29.284701
## iter 40 value 28.206434
## iter 50 value 28.154988
## iter 60 value 28.099348
## iter 70 value 26.717868
## iter 80 value 26.566399
## iter 90 value 25.854110
## iter 100 value 24.896935
## final value 24.896935
## stopped after 100 iterations
## # weights: 179
## initial value 115.288821
## iter 10 value 31.923634
## iter 20 value 4.685420
## iter 30 value 3.262050
## iter 40 value 2.869191
## iter 50 value 2.495505
## iter 60 value 1.388582
## iter 70 value 1.387001
## iter 80 value 1.386504
## iter 90 value 1.386453
## iter 100 value 1.386438
## final value 1.386438
## stopped after 100 iterations
## # weights: 39
## initial value 104.469913
## iter 10 value 66.610632
## iter 20 value 61.101961
## iter 30 value 60.470194
## iter 40 value 60.456492
## iter 50 value 60.455696
## final value 60.455694
## converged
## # weights: 109
## initial value 139.026525
## iter 10 value 71.763978
## iter 20 value 58.340810
## iter 30 value 42.122214
## iter 40 value 31.121237
## iter 50 value 21.540509
## iter 60 value 18.031058
## iter 70 value 17.034935
## iter 80 value 16.776358
## iter 90 value 16.747949
## iter 100 value 16.730930
## final value 16.730930
## stopped after 100 iterations
## # weights: 179
## initial value 108.681361
## iter 10 value 49.770746
## iter 20 value 25.196775
## iter 30 value 15.789021
## iter 40 value 13.325940
## iter 50 value 12.731487
## iter 60 value 12.389203
## iter 70 value 12.237870
## iter 80 value 12.230403
## iter 90 value 12.229750
## iter 100 value 12.229739
## final value 12.229739
## stopped after 100 iterations
## # weights: 39
## initial value 103.781789
## iter 10 value 69.854091
## iter 20 value 64.478612
## iter 30 value 64.455133
## iter 40 value 64.447088
## iter 50 value 64.246679
## iter 60 value 63.881565
## iter 70 value 60.092844
## iter 80 value 58.724277
## iter 90 value 58.414304
## iter 100 value 57.871346
## final value 57.871346
## stopped after 100 iterations
## # weights: 109
## initial value 110.858254
## iter 10 value 29.001877
## iter 20 value 16.789302
## iter 30 value 7.506211
## iter 40 value 6.219350
## iter 50 value 6.161157
## iter 60 value 4.059504
## iter 70 value 3.457172
## iter 80 value 3.435059
## iter 90 value 3.424010
## iter 100 value 3.317053
## final value 3.317053
## stopped after 100 iterations
## # weights: 179
## initial value 110.282140
## iter 10 value 28.800328
## iter 20 value 6.689439
## iter 30 value 4.789840
## iter 40 value 4.408558
## iter 50 value 4.382627
## iter 60 value 4.362895
## iter 70 value 4.331598
## iter 80 value 4.256061
## iter 90 value 4.225633
## iter 100 value 4.197079
## final value 4.197079
## stopped after 100 iterations
## # weights: 39
## initial value 104.199667
## iter 10 value 97.135992
## iter 20 value 56.562187
## iter 30 value 53.550246
## iter 40 value 52.804511
## iter 50 value 52.711283
## iter 60 value 51.891958
## final value 51.890736
## converged
## # weights: 109
## initial value 105.783987
## iter 10 value 36.051260
## iter 20 value 22.872095
## iter 30 value 20.441291
## iter 40 value 18.904560
## iter 50 value 16.815182
## iter 60 value 14.170870
## iter 70 value 13.590886
## iter 80 value 13.285516
## iter 90 value 12.084669
## iter 100 value 11.653061
## final value 11.653061
## stopped after 100 iterations
## # weights: 179
## initial value 96.573742
## iter 10 value 12.426671
## iter 20 value 2.603671
## iter 30 value 1.917845
## iter 40 value 1.910446
## iter 50 value 1.909701
## iter 60 value 1.909643
## iter 70 value 1.909593
## iter 80 value 1.909558
## final value 1.909547
## converged
## # weights: 39
## initial value 101.891132
## iter 10 value 69.822588
## iter 20 value 61.578350
## iter 30 value 60.159555
## iter 40 value 60.018445
## iter 50 value 59.867250
## iter 60 value 59.428108
## iter 70 value 58.797558
## iter 80 value 58.706134
## final value 58.706132
## converged
## # weights: 109
## initial value 110.075670
## iter 10 value 34.387351
## iter 20 value 24.792969
## iter 30 value 17.717945
## iter 40 value 16.213801
## iter 50 value 16.092456
## iter 60 value 16.084662
## iter 70 value 16.047551
## iter 80 value 16.037774
## iter 90 value 16.037331
## final value 16.037330
## converged
## # weights: 179
## initial value 115.708334
## iter 10 value 42.720787
## iter 20 value 20.745700
## iter 30 value 14.462041
## iter 40 value 12.080624
## iter 50 value 11.655244
## iter 60 value 11.635620
## iter 70 value 11.635378
## final value 11.635378
## converged
## # weights: 39
## initial value 106.069473
## iter 10 value 60.522302
## iter 20 value 54.527704
## iter 30 value 52.284471
## iter 40 value 52.245304
## iter 50 value 52.220860
## iter 60 value 52.217541
## iter 70 value 52.211214
## iter 80 value 52.199208
## iter 90 value 52.196297
## iter 100 value 51.913035
## final value 51.913035
## stopped after 100 iterations
## # weights: 109
## initial value 100.772932
## iter 10 value 29.274090
## iter 20 value 14.998718
## iter 30 value 9.514034
## iter 40 value 5.471462
## iter 50 value 3.668361
## iter 60 value 1.731531
## iter 70 value 1.034792
## iter 80 value 0.361360
## iter 90 value 0.240437
## iter 100 value 0.229935
## final value 0.229935
## stopped after 100 iterations
## # weights: 179
## initial value 131.203469
## iter 10 value 5.399975
## iter 20 value 1.865071
## iter 30 value 1.654823
## iter 40 value 1.633759
## iter 50 value 1.616382
## iter 60 value 0.897032
## iter 70 value 0.375729
## iter 80 value 0.288597
## iter 90 value 0.269633
## iter 100 value 0.244476
## final value 0.244476
## stopped after 100 iterations
## # weights: 39
## initial value 106.725442
## iter 10 value 67.053000
## iter 20 value 56.918741
## iter 30 value 46.879204
## iter 40 value 44.334662
## iter 50 value 42.518050
## iter 60 value 40.093907
## iter 70 value 38.287869
## iter 80 value 38.150847
## iter 90 value 38.133178
## iter 100 value 36.251203
## final value 36.251203
## stopped after 100 iterations
## # weights: 109
## initial value 99.236726
## iter 10 value 44.895310
## iter 20 value 21.492675
## iter 30 value 17.203205
## iter 40 value 13.691408
## iter 50 value 13.322819
## iter 60 value 13.232507
## iter 70 value 13.132122
## iter 80 value 13.099709
## iter 90 value 12.887211
## iter 100 value 12.884616
## final value 12.884616
## stopped after 100 iterations
## # weights: 179
## initial value 111.511383
## iter 10 value 18.072316
## iter 20 value 5.464337
## iter 30 value 3.728225
## iter 40 value 2.450664
## iter 50 value 2.254004
## iter 60 value 0.036160
## iter 70 value 0.011363
## iter 80 value 0.006209
## iter 90 value 0.004781
## iter 100 value 0.001038
## final value 0.001038
## stopped after 100 iterations
## # weights: 39
## initial value 100.861818
## iter 10 value 84.419478
## iter 20 value 67.939160
## iter 30 value 60.947374
## iter 40 value 57.497592
## iter 50 value 57.031335
## iter 60 value 56.438293
## iter 70 value 56.277117
## iter 80 value 55.736112
## iter 90 value 55.373229
## iter 100 value 54.703824
## final value 54.703824
## stopped after 100 iterations
## # weights: 109
## initial value 104.752870
## iter 10 value 44.024183
## iter 20 value 20.712667
## iter 30 value 19.062730
## iter 40 value 18.680577
## iter 50 value 18.546841
## iter 60 value 17.398069
## iter 70 value 15.545792
## iter 80 value 15.291764
## iter 90 value 15.287046
## final value 15.287042
## converged
## # weights: 179
## initial value 102.107527
## iter 10 value 41.754701
## iter 20 value 21.796409
## iter 30 value 14.222965
## iter 40 value 12.493327
## iter 50 value 11.733633
## iter 60 value 11.502781
## iter 70 value 11.442943
## iter 80 value 11.436353
## iter 90 value 11.435682
## iter 100 value 11.435662
## final value 11.435662
## stopped after 100 iterations
## # weights: 39
## initial value 108.891915
## iter 10 value 63.108813
## iter 20 value 55.576404
## iter 30 value 50.921987
## iter 40 value 50.207962
## iter 50 value 47.339088
## iter 60 value 44.101552
## iter 70 value 43.799540
## iter 80 value 43.781113
## iter 90 value 43.654310
## iter 100 value 38.693899
## final value 38.693899
## stopped after 100 iterations
## # weights: 109
## initial value 110.952063
## iter 10 value 37.488567
## iter 20 value 30.185653
## iter 30 value 29.821775
## iter 40 value 29.396215
## iter 50 value 27.824730
## iter 60 value 26.626484
## iter 70 value 22.601763
## iter 80 value 15.203914
## iter 90 value 13.401902
## iter 100 value 11.330811
## final value 11.330811
## stopped after 100 iterations
## # weights: 179
## initial value 99.249948
## iter 10 value 32.736158
## iter 20 value 3.701963
## iter 30 value 2.149204
## iter 40 value 2.120049
## iter 50 value 2.102357
## iter 60 value 2.084592
## iter 70 value 2.069249
## iter 80 value 2.057716
## iter 90 value 2.036711
## iter 100 value 1.525674
## final value 1.525674
## stopped after 100 iterations
## # weights: 39
## initial value 103.216005
## iter 10 value 74.740566
## iter 20 value 60.956468
## iter 30 value 57.996337
## iter 40 value 56.196639
## iter 50 value 55.792579
## iter 60 value 55.737553
## iter 70 value 55.725442
## iter 80 value 55.723004
## iter 90 value 55.722208
## iter 100 value 55.722007
## final value 55.722007
## stopped after 100 iterations
## # weights: 109
## initial value 99.992490
## iter 10 value 29.219300
## iter 20 value 18.717110
## iter 30 value 13.955319
## iter 40 value 9.876802
## iter 50 value 3.627456
## iter 60 value 2.611170
## iter 70 value 2.286633
## iter 80 value 2.072900
## iter 90 value 1.975204
## iter 100 value 1.944077
## final value 1.944077
## stopped after 100 iterations
## # weights: 179
## initial value 110.017516
## iter 10 value 21.954782
## iter 20 value 4.025205
## iter 30 value 0.244180
## iter 40 value 0.026057
## iter 50 value 0.012079
## iter 60 value 0.004546
## iter 70 value 0.001620
## iter 80 value 0.000766
## iter 90 value 0.000462
## iter 100 value 0.000211
## final value 0.000211
## stopped after 100 iterations
## # weights: 39
## initial value 107.890868
## iter 10 value 78.820789
## iter 20 value 72.753054
## iter 30 value 67.916754
## iter 40 value 61.769060
## iter 50 value 59.796064
## iter 60 value 58.088285
## iter 70 value 58.019691
## final value 58.019672
## converged
## # weights: 109
## initial value 111.946860
## iter 10 value 48.955522
## iter 20 value 26.266252
## iter 30 value 18.749171
## iter 40 value 17.163630
## iter 50 value 16.666293
## iter 60 value 16.574378
## iter 70 value 16.568674
## final value 16.568664
## converged
## # weights: 179
## initial value 120.143642
## iter 10 value 33.821112
## iter 20 value 21.411552
## iter 30 value 14.814050
## iter 40 value 13.566355
## iter 50 value 12.211218
## iter 60 value 11.840829
## iter 70 value 11.834729
## iter 80 value 11.834604
## final value 11.834603
## converged
## # weights: 39
## initial value 99.321827
## iter 10 value 64.839847
## iter 20 value 55.493800
## iter 30 value 47.528062
## iter 40 value 43.593625
## iter 50 value 41.135095
## iter 60 value 40.483243
## iter 70 value 39.581304
## iter 80 value 37.838053
## iter 90 value 37.656372
## iter 100 value 37.570377
## final value 37.570377
## stopped after 100 iterations
## # weights: 109
## initial value 98.614268
## iter 10 value 35.386405
## iter 20 value 27.307056
## iter 30 value 10.182678
## iter 40 value 6.478050
## iter 50 value 5.518198
## iter 60 value 5.451700
## iter 70 value 5.337942
## iter 80 value 5.211909
## iter 90 value 5.140635
## iter 100 value 5.128243
## final value 5.128243
## stopped after 100 iterations
## # weights: 179
## initial value 104.556780
## iter 10 value 28.861421
## iter 20 value 16.359042
## iter 30 value 12.171576
## iter 40 value 9.095570
## iter 50 value 7.948036
## iter 60 value 6.018246
## iter 70 value 4.617903
## iter 80 value 3.868424
## iter 90 value 2.484282
## iter 100 value 2.076188
## final value 2.076188
## stopped after 100 iterations
## # weights: 39
## initial value 108.809362
## iter 10 value 82.282259
## iter 20 value 79.054473
## iter 30 value 76.648013
## iter 40 value 73.668901
## iter 50 value 68.440641
## iter 60 value 64.459788
## iter 70 value 62.144393
## iter 80 value 61.681460
## iter 90 value 60.005582
## iter 100 value 55.357053
## final value 55.357053
## stopped after 100 iterations
## # weights: 109
## initial value 105.132695
## iter 10 value 28.650164
## iter 20 value 13.742007
## iter 30 value 10.921882
## iter 40 value 10.882696
## iter 50 value 10.882002
## iter 60 value 10.881369
## iter 70 value 10.229645
## iter 80 value 10.227415
## final value 10.227412
## converged
## # weights: 179
## initial value 109.845005
## iter 10 value 60.807257
## iter 20 value 26.762643
## iter 30 value 16.621174
## iter 40 value 13.121577
## iter 50 value 6.575265
## iter 60 value 5.898170
## iter 70 value 4.484082
## iter 80 value 4.023736
## iter 90 value 4.004326
## iter 100 value 3.979873
## final value 3.979873
## stopped after 100 iterations
## # weights: 39
## initial value 105.537692
## iter 10 value 69.110092
## iter 20 value 62.716633
## iter 30 value 60.195828
## iter 40 value 59.201423
## iter 50 value 58.486384
## iter 60 value 58.282270
## iter 70 value 57.819131
## final value 57.808905
## converged
## # weights: 109
## initial value 105.149562
## iter 10 value 51.660696
## iter 20 value 34.190558
## iter 30 value 24.122861
## iter 40 value 18.121648
## iter 50 value 17.216555
## iter 60 value 17.125182
## iter 70 value 16.624065
## iter 80 value 16.427706
## iter 90 value 16.333064
## iter 100 value 16.318269
## final value 16.318269
## stopped after 100 iterations
## # weights: 179
## initial value 121.698118
## iter 10 value 57.422570
## iter 20 value 26.810319
## iter 30 value 16.653453
## iter 40 value 14.301596
## iter 50 value 13.403715
## iter 60 value 12.803918
## iter 70 value 12.610607
## iter 80 value 12.594299
## iter 90 value 12.589890
## iter 100 value 12.588611
## final value 12.588611
## stopped after 100 iterations
## # weights: 39
## initial value 106.626626
## iter 10 value 74.087800
## iter 20 value 62.536005
## iter 30 value 55.653646
## iter 40 value 51.206462
## iter 50 value 47.699505
## iter 60 value 47.402029
## iter 70 value 47.358950
## iter 80 value 47.354316
## iter 90 value 47.353375
## iter 100 value 47.352791
## final value 47.352791
## stopped after 100 iterations
## # weights: 109
## initial value 97.909507
## iter 10 value 32.940182
## iter 20 value 16.448580
## iter 30 value 11.752310
## iter 40 value 10.351772
## iter 50 value 9.825231
## iter 60 value 8.384007
## iter 70 value 8.220313
## iter 80 value 7.837716
## iter 90 value 7.736864
## iter 100 value 7.616654
## final value 7.616654
## stopped after 100 iterations
## # weights: 179
## initial value 110.939234
## iter 10 value 18.896341
## iter 20 value 6.357458
## iter 30 value 5.017966
## iter 40 value 3.604925
## iter 50 value 3.498478
## iter 60 value 3.478419
## iter 70 value 2.912463
## iter 80 value 2.137091
## iter 90 value 2.104093
## iter 100 value 2.090416
## final value 2.090416
## stopped after 100 iterations
## # weights: 39
## initial value 107.431463
## iter 10 value 94.081265
## iter 20 value 74.152160
## iter 30 value 73.072557
## iter 40 value 70.888173
## iter 50 value 69.574502
## iter 60 value 66.841615
## iter 70 value 66.360837
## iter 80 value 60.878642
## iter 90 value 59.911318
## iter 100 value 59.751474
## final value 59.751474
## stopped after 100 iterations
## # weights: 109
## initial value 119.777607
## iter 10 value 38.426096
## iter 20 value 13.419502
## iter 30 value 12.927932
## iter 40 value 12.715726
## iter 50 value 10.540310
## iter 60 value 10.017055
## iter 70 value 6.891419
## iter 80 value 6.776533
## iter 90 value 6.773448
## iter 100 value 6.772103
## final value 6.772103
## stopped after 100 iterations
## # weights: 179
## initial value 103.608488
## iter 10 value 13.368798
## iter 20 value 0.634417
## iter 30 value 0.012090
## final value 0.000063
## converged
## # weights: 39
## initial value 99.676914
## iter 10 value 66.641104
## iter 20 value 61.282028
## iter 30 value 59.915630
## iter 40 value 59.679407
## iter 50 value 58.891108
## iter 60 value 58.070548
## iter 70 value 57.102715
## iter 80 value 56.908231
## iter 90 value 56.904636
## final value 56.904624
## converged
## # weights: 109
## initial value 111.619633
## iter 10 value 64.198248
## iter 20 value 40.050037
## iter 30 value 27.964938
## iter 40 value 18.581947
## iter 50 value 16.362595
## iter 60 value 15.816054
## iter 70 value 15.632172
## iter 80 value 15.613596
## iter 90 value 15.612880
## iter 100 value 15.612768
## final value 15.612768
## stopped after 100 iterations
## # weights: 179
## initial value 119.434561
## iter 10 value 33.830236
## iter 20 value 16.722051
## iter 30 value 13.713529
## iter 40 value 13.425083
## iter 50 value 13.070572
## iter 60 value 12.398017
## iter 70 value 12.326190
## iter 80 value 12.324373
## final value 12.324340
## converged
## # weights: 39
## initial value 111.185498
## iter 10 value 67.513777
## iter 20 value 59.708386
## iter 30 value 56.224334
## iter 40 value 55.837657
## iter 50 value 55.779602
## iter 60 value 55.668235
## iter 70 value 55.571100
## iter 80 value 55.510237
## iter 90 value 54.470742
## iter 100 value 54.270373
## final value 54.270373
## stopped after 100 iterations
## # weights: 109
## initial value 95.403886
## iter 10 value 25.218678
## iter 20 value 9.718044
## iter 30 value 9.239404
## iter 40 value 9.204647
## iter 50 value 9.115589
## iter 60 value 9.005080
## iter 70 value 8.971842
## iter 80 value 8.826047
## iter 90 value 8.819690
## iter 100 value 8.588028
## final value 8.588028
## stopped after 100 iterations
## # weights: 179
## initial value 124.887593
## iter 10 value 34.242865
## iter 20 value 12.542742
## iter 30 value 11.147125
## iter 40 value 10.449822
## iter 50 value 5.399940
## iter 60 value 2.657580
## iter 70 value 2.576438
## iter 80 value 2.537059
## iter 90 value 2.501625
## iter 100 value 1.645524
## final value 1.645524
## stopped after 100 iterations
## # weights: 39
## initial value 108.058228
## iter 10 value 66.576457
## iter 20 value 59.831923
## iter 30 value 55.867867
## iter 40 value 55.145270
## iter 50 value 54.504772
## iter 60 value 53.764545
## iter 70 value 53.190535
## iter 80 value 52.611680
## iter 90 value 51.930103
## iter 100 value 51.594752
## final value 51.594752
## stopped after 100 iterations
## # weights: 109
## initial value 108.108231
## iter 10 value 34.286724
## iter 20 value 21.783733
## iter 30 value 16.753119
## iter 40 value 12.993231
## iter 50 value 10.432526
## iter 60 value 10.348286
## iter 70 value 10.342348
## iter 80 value 10.341741
## iter 90 value 10.341601
## final value 10.341593
## converged
## # weights: 179
## initial value 109.183174
## iter 10 value 26.022158
## iter 20 value 6.976148
## iter 30 value 6.169617
## iter 40 value 6.159280
## iter 50 value 5.085302
## iter 60 value 2.780161
## iter 70 value 2.774497
## iter 80 value 2.773641
## iter 90 value 2.773408
## iter 100 value 2.770845
## final value 2.770845
## stopped after 100 iterations
## # weights: 39
## initial value 104.882029
## iter 10 value 79.226375
## iter 20 value 66.130154
## iter 30 value 62.480330
## iter 40 value 60.452157
## iter 50 value 59.870426
## iter 60 value 59.529400
## iter 70 value 59.512856
## iter 80 value 59.372216
## iter 90 value 59.074647
## iter 100 value 59.011622
## final value 59.011622
## stopped after 100 iterations
## # weights: 109
## initial value 108.260308
## iter 10 value 71.546898
## iter 20 value 46.979786
## iter 30 value 25.038661
## iter 40 value 18.233025
## iter 50 value 17.165942
## iter 60 value 17.103759
## iter 70 value 17.097545
## iter 80 value 17.097418
## final value 17.097416
## converged
## # weights: 179
## initial value 108.514646
## iter 10 value 40.516515
## iter 20 value 17.317074
## iter 30 value 13.212413
## iter 40 value 12.735248
## iter 50 value 12.601368
## iter 60 value 12.534321
## iter 70 value 12.439698
## iter 80 value 12.402127
## iter 90 value 12.401633
## final value 12.401632
## converged
## # weights: 39
## initial value 107.236716
## iter 10 value 61.606762
## iter 20 value 54.433158
## iter 30 value 51.623683
## iter 40 value 51.602323
## iter 50 value 51.592410
## iter 60 value 51.582496
## iter 70 value 51.579357
## iter 80 value 51.568045
## iter 90 value 51.566858
## iter 100 value 51.561059
## final value 51.561059
## stopped after 100 iterations
## # weights: 109
## initial value 110.666515
## iter 10 value 55.952169
## iter 20 value 20.488263
## iter 30 value 7.286823
## iter 40 value 4.323604
## iter 50 value 4.307191
## iter 60 value 4.289647
## iter 70 value 4.279134
## iter 80 value 4.266235
## iter 90 value 4.258850
## iter 100 value 2.057594
## final value 2.057594
## stopped after 100 iterations
## # weights: 179
## initial value 116.198662
## iter 10 value 44.484663
## iter 20 value 16.848190
## iter 30 value 14.185958
## iter 40 value 13.854471
## iter 50 value 13.762603
## iter 60 value 13.622751
## iter 70 value 13.530163
## iter 80 value 13.336184
## iter 90 value 13.225257
## iter 100 value 12.918183
## final value 12.918183
## stopped after 100 iterations
## # weights: 179
## initial value 136.763563
## iter 10 value 41.095424
## iter 20 value 22.265719
## iter 30 value 14.637937
## iter 40 value 13.449071
## iter 50 value 12.927608
## iter 60 value 12.774159
## iter 70 value 12.768531
## iter 80 value 12.767823
## iter 90 value 12.767793
## final value 12.767792
## converged## NB
nb_fit <- train(`PAM50.mRNA`~., data = data_norm, method = "nb",
trControl = fitControl)
## rf
rf_fit<- train(`PAM50.mRNA`~., data = data_norm, method = "rf",
trControl = fitControl)print(svm_fit)## Support Vector Machines with Linear Kernel
##
## 80 samples
## 30 predictors
## 4 classes: 'Basal.like', 'HER2.enriched', 'Luminal.A', 'Luminal.B'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 10 times)
## Summary of sample sizes: 72, 71, 73, 72, 74, 71, ...
## Resampling results:
##
## Accuracy Kappa Mean_F1 Mean_Sensitivity Mean_Specificity
## 0.773627 0.6904643 0.8352319 0.75375 0.9240625
## Mean_Pos_Pred_Value Mean_Neg_Pred_Value Mean_Precision Mean_Recall
## 0.8312189 0.9320476 0.8312189 0.75375
## Mean_Detection_Rate Mean_Balanced_Accuracy
## 0.1934067 0.8389062
##
## Tuning parameter 'C' was held constant at a value of 1print(nn_fit)## Neural Network
##
## 80 samples
## 30 predictors
## 4 classes: 'Basal.like', 'HER2.enriched', 'Luminal.A', 'Luminal.B'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 10 times)
## Summary of sample sizes: 73, 72, 73, 72, 71, 71, ...
## Resampling results across tuning parameters:
##
## size decay Accuracy Kappa Mean_F1 Mean_Sensitivity
## 1 0e+00 0.4472341 0.2358024 NaN 0.4129167
## 1 1e-04 0.4605357 0.2544349 0.7333333 0.4229167
## 1 1e-01 0.4891270 0.2907450 NaN 0.4345833
## 3 0e+00 0.6687024 0.5472742 0.8074968 0.6454167
## 3 1e-04 0.6853690 0.5716715 0.8136905 0.6675000
## 3 1e-01 0.7526468 0.6616359 0.8332393 0.7362500
## 5 0e+00 0.7288532 0.6291137 0.8144093 0.7095833
## 5 1e-04 0.7279405 0.6288051 0.8103571 0.7141667
## 5 1e-01 0.7535198 0.6617535 0.8208433 0.7362500
## Mean_Specificity Mean_Pos_Pred_Value Mean_Neg_Pred_Value Mean_Precision
## 0.8095685 0.2013889 0.8430345 0.2013889
## 0.8140060 0.3437500 0.8453052 0.3437500
## 0.8236101 NaN 0.8503482 NaN
## 0.8889226 0.7077381 0.8997837 0.7077381
## 0.8952708 0.7687147 0.9034683 0.7687147
## 0.9169792 0.8092857 0.9243720 0.8092857
## 0.9088750 0.7972854 0.9175198 0.7972854
## 0.9085863 0.7939394 0.9170089 0.7939394
## 0.9163363 0.8301329 0.9250337 0.8301329
## Mean_Recall Mean_Detection_Rate Mean_Balanced_Accuracy
## 0.4129167 0.1118085 0.6112426
## 0.4229167 0.1151339 0.6184613
## 0.4345833 0.1222817 0.6290967
## 0.6454167 0.1671756 0.7671696
## 0.6675000 0.1713423 0.7813854
## 0.7362500 0.1881617 0.8266146
## 0.7095833 0.1822133 0.8092292
## 0.7141667 0.1819851 0.8113765
## 0.7362500 0.1883800 0.8262932
##
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were size = 5 and decay = 0.1.print(nb_fit)## Naive Bayes
##
## 80 samples
## 30 predictors
## 4 classes: 'Basal.like', 'HER2.enriched', 'Luminal.A', 'Luminal.B'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 10 times)
## Summary of sample sizes: 73, 72, 72, 71, 73, 72, ...
## Resampling results across tuning parameters:
##
## usekernel Accuracy Kappa Mean_F1 Mean_Sensitivity
## FALSE 0.8373254 0.7768836 0.8669276 0.8158333
## TRUE 0.7897500 0.7110541 0.8270833 0.7725000
## Mean_Specificity Mean_Pos_Pred_Value Mean_Neg_Pred_Value Mean_Precision
## 0.9461488 0.8716564 0.9516062 0.8716564
## 0.9291667 0.8435065 0.9370962 0.8435065
## Mean_Recall Mean_Detection_Rate Mean_Balanced_Accuracy
## 0.8158333 0.2093313 0.8809911
## 0.7725000 0.1974375 0.8508333
##
## Tuning parameter 'fL' was held constant at a value of 0
## Tuning
## parameter 'adjust' was held constant at a value of 1
## Accuracy was used to select the optimal model using the largest value.
## The final values used for the model were fL = 0, usekernel = FALSE and adjust
## = 1.print(rf_fit)## Random Forest
##
## 80 samples
## 30 predictors
## 4 classes: 'Basal.like', 'HER2.enriched', 'Luminal.A', 'Luminal.B'
##
## No pre-processing
## Resampling: Cross-Validated (10 fold, repeated 10 times)
## Summary of sample sizes: 72, 72, 73, 71, 72, 72, ...
## Resampling results across tuning parameters:
##
## mtry Accuracy Kappa Mean_F1 Mean_Sensitivity Mean_Specificity
## 2 0.7795238 0.6920946 0.8228571 0.7362500 0.9227470
## 16 0.7430873 0.6407666 0.8059382 0.6979167 0.9101488
## 30 0.7337460 0.6283982 0.7890110 0.6891667 0.9067500
## Mean_Pos_Pred_Value Mean_Neg_Pred_Value Mean_Precision Mean_Recall
## 0.8804167 0.9350367 0.8804167 0.7362500
## 0.8441123 0.9238899 0.8441123 0.6979167
## 0.8331349 0.9207579 0.8331349 0.6891667
## Mean_Detection_Rate Mean_Balanced_Accuracy
## 0.1948810 0.8294985
## 0.1857718 0.8040327
## 0.1834365 0.7979583
##
## Accuracy was used to select the optimal model using the largest value.
## The final value used for the model was mtry = 2.Before, K-fold cross validation, The SVM model achieved a accuracy of 76% The neural networks model achieved accuracy of 71% The naive bayes model achieved a accuracy of 76%
k-Fold Cross Validation is done for the whole dataset. I have used k = 10 which means 10 folds take place along with 10 repetitions. For testing the data, I have used 3 models to test the k-fold CV
(10 fold, repeated 10 times)
- SVM :
Accuracy - 0.773627 Kappa - 0.6904643
Mean_F1 - 0.8352319 Mean_Sensitivity - 0.75375
Mean_Specificity - 0.9240625 Mean_Precision - 0.8312189 Mean_Recall -
0.75375
There’s no much change after applying k-fold cross validation
- Neural Networks :
Accuracy - 0.7786581 Kappa - 0.6980310
Mean_F1 - 0.7569600 Mean_Sensitivity - 0.7568452
Mean_Specificity - 0.9250575 Mean_Precision - 0.7997022 Mean_Recall -
0.7568452
Naive baye’s : Accuracy - 0.7535198 Kappa - 0.6617535
Mean_F1 - 0.8208433 Mean_Sensitivity - 0.7362500
Mean_Specificity - 0.8410706 Mean_Precision - 0.9163363 Mean_Recall - 0.7362500Random Forests:
Accuracy - 0.7795238 Kappa - 0.6920946 Mean_F1 - 0.8228571
Mean_Sensitivity - 0.7362500
Mean_Specificity - 0.9227470 Mean_Precision - 0.8331349
Mean_Recall - 0.6891667
HYPERPARAMETER TUNING
- SVM
The tuning and training of an SVM with a linear kernel is demonstrated in the code below, which also controls crossvalidation for tuning the hyperparameter C.
set.seed(10)
#Configuring train control for cross validation and hyperparameter calibration
train_control <- trainControl(method="repeatedcv", number=10, repeats=10, savePredictions = TRUE, summaryFunction = multiClassSummary)
#Tunegrid for various C values
grid <- expand.grid(C = seq(0.000001,0.15,0.002))
set.seed(10)
svm.lin.mod <- train(PAM50.mRNA ~ ., data=data_norm[samp,], trControl=train_control, method="svmLinear", preProcess = c("center","scale"), tuneGrid =grid, tuneLength = 10)
svm.predicts <- predict(svm.lin.mod, newdata = data_norm[-samp,])
confusionMatrix(svm.predicts, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 5 2 0 0
## HER2.enriched 0 1 0 0
## Luminal.A 0 0 5 1
## Luminal.B 0 0 1 6
##
## Overall Statistics
##
## Accuracy : 0.8095
## 95% CI : (0.5809, 0.9455)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 1.026e-05
##
## Kappa : 0.7358
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 1.0000 0.33333 0.8333
## Specificity 0.8750 1.00000 0.9333
## Pos Pred Value 0.7143 1.00000 0.8333
## Neg Pred Value 1.0000 0.90000 0.9333
## Precision 0.7143 1.00000 0.8333
## Recall 1.0000 0.33333 0.8333
## F1 0.8333 0.50000 0.8333
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.2381 0.04762 0.2381
## Detection Prevalence 0.3333 0.04762 0.2857
## Balanced Accuracy 0.9375 0.66667 0.8833
## Class: Luminal.B
## Sensitivity 0.8571
## Specificity 0.9286
## Pos Pred Value 0.8571
## Neg Pred Value 0.9286
## Precision 0.8571
## Recall 0.8571
## F1 0.8571
## Prevalence 0.3333
## Detection Rate 0.2857
## Detection Prevalence 0.3333
## Balanced Accuracy 0.8929The SVM model achieved accuracy of 80% after hyper-parameter tuning. Therefore, no much change after tuning
NN
set.seed(5000)
cctrlR <- trainControl(method = "repeatedcv", number=10, repeats=10, returnResamp = "all", search = "random")
nn <- train(PAM50.mRNA ~., data= data_norm[samp,],
method = "nnet",
trControl = cctrlR,
preProc = c("center", "scale"),
trace = FALSE)
nnpred <- predict(nn, newdata= data_norm[-samp,])
#viewing confusion matrix
confusionMatrix(nnpred, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 5 1 0 0
## HER2.enriched 0 1 0 0
## Luminal.A 0 0 6 2
## Luminal.B 0 1 0 5
##
## Overall Statistics
##
## Accuracy : 0.8095
## 95% CI : (0.5809, 0.9455)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 1.026e-05
##
## Kappa : 0.7358
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 1.0000 0.33333 1.0000
## Specificity 0.9375 1.00000 0.8667
## Pos Pred Value 0.8333 1.00000 0.7500
## Neg Pred Value 1.0000 0.90000 1.0000
## Precision 0.8333 1.00000 0.7500
## Recall 1.0000 0.33333 1.0000
## F1 0.9091 0.50000 0.8571
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.2381 0.04762 0.2857
## Detection Prevalence 0.2857 0.04762 0.3810
## Balanced Accuracy 0.9688 0.66667 0.9333
## Class: Luminal.B
## Sensitivity 0.7143
## Specificity 0.9286
## Pos Pred Value 0.8333
## Neg Pred Value 0.8667
## Precision 0.8333
## Recall 0.7143
## F1 0.7692
## Prevalence 0.3333
## Detection Rate 0.2381
## Detection Prevalence 0.2857
## Balanced Accuracy 0.8214The model achieved accuracy of 80.1% after hyper parameter tuning
nb1 <- train(`PAM50.mRNA`~., data = data_norm[samp,], method = "nb",
trControl = trainControl(method = "repeatedcv", number=10, repeats=10),
tuneGrid = data.frame(usekernel = TRUE, fL = 0.5, adjust = 5))
bps <- predict(nb1, newdata=data_norm[-samp,])
nbpred <- predict(nb1, newdata= data_norm[-samp,])
#viewing confusion matrix
confusionMatrix(nbpred, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 5 3 0 1
## HER2.enriched 0 0 0 0
## Luminal.A 0 0 5 1
## Luminal.B 0 0 1 5
##
## Overall Statistics
##
## Accuracy : 0.7143
## 95% CI : (0.4782, 0.8872)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 0.0004045
##
## Kappa : 0.6038
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 1.0000 0.0000 0.8333
## Specificity 0.7500 1.0000 0.9333
## Pos Pred Value 0.5556 NaN 0.8333
## Neg Pred Value 1.0000 0.8571 0.9333
## Precision 0.5556 NA 0.8333
## Recall 1.0000 0.0000 0.8333
## F1 0.7143 NA 0.8333
## Prevalence 0.2381 0.1429 0.2857
## Detection Rate 0.2381 0.0000 0.2381
## Detection Prevalence 0.4286 0.0000 0.2857
## Balanced Accuracy 0.8750 0.5000 0.8833
## Class: Luminal.B
## Sensitivity 0.7143
## Specificity 0.9286
## Pos Pred Value 0.8333
## Neg Pred Value 0.8667
## Precision 0.8333
## Recall 0.7143
## F1 0.7692
## Prevalence 0.3333
## Detection Rate 0.2381
## Detection Prevalence 0.2857
## Balanced Accuracy 0.8214The model achieved 71.4% accuracy after hyper parameter tuning
rb <- train(`PAM50.mRNA`~., data = data_norm[samp,], method = "rf",
trControl = trainControl(method = "repeatedcv", number=10, repeats=10))
rbps <- predict(rb, newdata=data_norm[-samp,])
#viewing confusion matrix
confusionMatrix(rbps, factor(data_norm$PAM50.mRNA[-samp]), mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 4 1 0 1
## HER2.enriched 0 1 0 0
## Luminal.A 1 0 5 2
## Luminal.B 0 1 1 4
##
## Overall Statistics
##
## Accuracy : 0.6667
## 95% CI : (0.4303, 0.8541)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 0.001827
##
## Kappa : 0.5377
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 0.8000 0.33333 0.8333
## Specificity 0.8750 1.00000 0.8000
## Pos Pred Value 0.6667 1.00000 0.6250
## Neg Pred Value 0.9333 0.90000 0.9231
## Precision 0.6667 1.00000 0.6250
## Recall 0.8000 0.33333 0.8333
## F1 0.7273 0.50000 0.7143
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.1905 0.04762 0.2381
## Detection Prevalence 0.2857 0.04762 0.3810
## Balanced Accuracy 0.8375 0.66667 0.8167
## Class: Luminal.B
## Sensitivity 0.5714
## Specificity 0.8571
## Pos Pred Value 0.6667
## Neg Pred Value 0.8000
## Precision 0.6667
## Recall 0.5714
## F1 0.6154
## Prevalence 0.3333
## Detection Rate 0.1905
## Detection Prevalence 0.2857
## Balanced Accuracy 0.7143The model achieved 66.6% accuracy after hyper parameter tuning
The SVM model achieved a accuracy of 80% and the accuracy remained same after hyper-parameter tuning The neural networks model achieved accuracy of 80% and the accuracy remained the same after hyper-parameter tuning The naive bayes model achieved a accuracy of 76% and the accuracy decreased to 71.4% after hyper-parameter tuning The Random Forest model achieved a accuracy of 61% and the accuracy increased to 66.6% after hyper-parameter tuning
Comparison of models after tuning
- It is observed by comparing Accuracy that SVM and NNmodel performs the best amongst the others both before tuning and after tuning.
Compairing Model Accuracies:
SVM on original data set values : Accuracy : 0.809, Kappa : 0.7399 Neural Networks on original data set values : Accuracy : 0.809, Kappa : 0.7358 Naive bayes on original data set values : Accuracy : 0.7619, Kappa : 0.6729 Random Forest on original data set values : Accuracy : 0.619, Kappa : 0.4734
SVM with k-fold cross validation : Accuracy : 0.7723365, Kappa : 0.6914544 Neural Networks with k-fold cross validation : Accuracy : 0.7786581, Kappa : 0.6980310 Naive bayes with k-fold cross validation :Accuracy : 0.7535198, Kappa : 0.6617535 Random Forest with k-fold cross validation :Accuracy : 0.7795238, Kappa : 0.6920946
SVM on original data set values after hyper parameter tuning : Accuracy : 0.8095, Kappa : 0.7358 Neural Networks on original data set values after hyper parameter tuning : 0.8095 , Kappa : 0.7358 Naive bayes on original data set values after hyper parameter tuning : Accuracy : 0.7143, Kappa : 0.6038 Random Forest on original data set values after hyper parameter tuning : Accuracy : 0.6667, Kappa : 0.5377
-From the above, we can say that both SVM and Neural Network model has done a good job predicting the cancer subtype
Deployment
- bagging : use of bagging with homogeneous learners
- Stacked ensemble using - SVM, Neural Networks, Naive Bayes - Majority voting
- boosting : Extreme Gradient Boosting
Bagging
use of bagging with homogeneous learners
set.seed(1000)
#fit the bagged model
bag <- bagging(formula = PAM50.mRNA ~ .,data = train,nbagg = 150, coob = TRUE,control = rpart.control(minsplit = 2, cp = 0))
#display fitted bagged model
bag##
## Bagging classification trees with 150 bootstrap replications
##
## Call: bagging.data.frame(formula = PAM50.mRNA ~ ., data = train, nbagg = 150,
## coob = TRUE, control = rpart.control(minsplit = 2, cp = 0))
##
## Out-of-bag estimate of misclassification error: 0.2542bag_pred <- predict(bag, test)
confusionMatrix(test$`PAM50.mRNA`,bag_pred)## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 4 0 0 1
## HER2.enriched 1 1 0 1
## Luminal.A 2 0 4 0
## Luminal.B 1 0 2 4
##
## Overall Statistics
##
## Accuracy : 0.619
## 95% CI : (0.3844, 0.8189)
## No Information Rate : 0.381
## P-Value [Acc > NIR] : 0.02313
##
## Kappa : 0.475
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 0.5000 1.00000 0.6667
## Specificity 0.9231 0.90000 0.8667
## Pos Pred Value 0.8000 0.33333 0.6667
## Neg Pred Value 0.7500 1.00000 0.8667
## Prevalence 0.3810 0.04762 0.2857
## Detection Rate 0.1905 0.04762 0.1905
## Detection Prevalence 0.2381 0.14286 0.2857
## Balanced Accuracy 0.7115 0.95000 0.7667
## Class: Luminal.B
## Sensitivity 0.6667
## Specificity 0.8000
## Pos Pred Value 0.5714
## Neg Pred Value 0.8571
## Prevalence 0.2857
## Detection Rate 0.1905
## Detection Prevalence 0.3333
## Balanced Accuracy 0.7333Accuracy achieved using bagging is 0.619
Stacking
The breast cancer sub-type classifier will be deployed as a model ensemble. A model ensemble is a prediction model that is an aggregate of a set of models. Specifically, a model ensemble aggregates predictions across all the individual models in the ensemble using a voting mechanism. In general, we expect that a collection of independent models would perform better than any individual model. The voting mechanism that will be used for the breast cancer ensemble is the mode prediction for an patient across the three models.
Define a function to calculate the mode across values.
Mode <- function(x) {
ux <- unique(x)
ux[which.max(tabulate(match(x, ux)))]
}Next, define a function to loop through the observations in the test data and generate the modal prediction for each observation across the three classifiers.
vote <- function (p1, p2, p3) {
m <- length(p1) # number of predictions in the test data
ds <- numeric(m) # creates numeric vector to hold final prediction
# loops through predictions in the test data
for (i in 1:m) {
# calculate mode prediction for an obs across classifiers
p <- c(p1[i],p2[i],p3[i])
# store modal prediction in return vector
ds[i] <- Mode(p)
}
# return vector
return(ds)
}Use functions to generate the model ensemble.
ens_pred <- vote(p1 = svm_pred, p2 = nnpred_model2, p3 = nbpred_model3)
ens_pred## [1] 1 1 1 1 1 2 2 4 3 3 3 3 3 3 4 1 4 4 4 3 4# Factor
ens_pred[which(ens_pred == 1)] = "Basal.like"
ens_pred[which(ens_pred == 2)] = "HER2.enriched"
ens_pred[which(ens_pred == 3)] = "Luminal.A"
ens_pred[which(ens_pred == 4)] = "Luminal.B"label <- factor(test$PAM50.mRNA)
label## [1] Basal.like Basal.like Basal.like Basal.like Basal.like
## [6] HER2.enriched HER2.enriched HER2.enriched Luminal.A Luminal.A
## [11] Luminal.A Luminal.A Luminal.A Luminal.A Luminal.B
## [16] Luminal.B Luminal.B Luminal.B Luminal.B Luminal.B
## [21] Luminal.B
## Levels: Basal.like HER2.enriched Luminal.A Luminal.BconfusionMatrix(factor(ens_pred), label , mode = "everything")## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 5 0 0 1
## HER2.enriched 0 2 0 0
## Luminal.A 0 0 6 1
## Luminal.B 0 1 0 5
##
## Overall Statistics
##
## Accuracy : 0.8571
## 95% CI : (0.6366, 0.9695)
## No Information Rate : 0.3333
## P-Value [Acc > NIR] : 1.102e-06
##
## Kappa : 0.8037
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 1.0000 0.66667 1.0000
## Specificity 0.9375 1.00000 0.9333
## Pos Pred Value 0.8333 1.00000 0.8571
## Neg Pred Value 1.0000 0.94737 1.0000
## Precision 0.8333 1.00000 0.8571
## Recall 1.0000 0.66667 1.0000
## F1 0.9091 0.80000 0.9231
## Prevalence 0.2381 0.14286 0.2857
## Detection Rate 0.2381 0.09524 0.2857
## Detection Prevalence 0.2857 0.09524 0.3333
## Balanced Accuracy 0.9688 0.83333 0.9667
## Class: Luminal.B
## Sensitivity 0.7143
## Specificity 0.9286
## Pos Pred Value 0.8333
## Neg Pred Value 0.8667
## Precision 0.8333
## Recall 0.7143
## F1 0.7692
## Prevalence 0.3333
## Detection Rate 0.2381
## Detection Prevalence 0.2857
## Balanced Accuracy 0.8214mv_auc <- multiclass.roc(label, as.ordered(ens_pred))
auc(mv_auc)## Multi-class area under the curve: 0.8452Macro-averaged Metrics : The per-class metrics can be averaged over all the classes resulting in macro-averaged precision, recall and F-1.
# macro-averaged precision
precision_stack<- c(0.8333,1.00000,0.8571,0.8333)
macro_precision_stack <- mean(precision_stack)
# macro-averaged recall
recall_stack <- c(1.0000,0.66667,1.0000,0.7143)
macro_recall_stack<- mean(recall_stack)
# macro-averaged F-1
F1_stack<- c(0.9091,0.80000,0.9231,0.7692)
macroF1_stack <- mean(F1_stack)
macro_average_stack <-data.frame(macro_precision_stack, macro_recall_stack, macroF1_stack)
macro_average_stackName_metrics <- c("Accuracy", "Precision", "Recall", "F-1", "AUC", "Kappa")
values_stack <- c(0.8571, 0.880925, 0.8452425, 0.85035, 0.8452, 0.8037)
metrics_stack <- data.frame(Name_metrics, values_stack)
print (metrics_stack)## Name_metrics values_stack
## 1 Accuracy 0.8571000
## 2 Precision 0.8809250
## 3 Recall 0.8452425
## 4 F-1 0.8503500
## 5 AUC 0.8452000
## 6 Kappa 0.8037000comparison of ensemble to individual models
Of the four models (SVM, NN, NB, RF)- The SVM and NN works better and both the models have similar Accuracy, Precision, Recall, F-1, AUC and Kappa Values
Now, comparing Accuracy, Precision, Recall, F-1, AUC and Kappa values with that of stacked ensemble model (SVM, NN, NB)
I have saved the Accuracy, Precision, Recall, F-1, AUC and Kappa values under metric_model
metrics_svmmetrics_nnmetrics_stackBy comparing above values, we can say that stacked ensemble model
worked better than other individual models with the highest accuracy of
85.7% and Precision-0.8809250, Recall-0.8452425, F-1-0.8503500, AUC -
0.8452000, Kappa - 0.8037000
Therefore, These values are higher than individual models.
Conclusion : A model ensemble is a prediction model that is an aggregate of a set of models. Specifically, a model ensemble aggregates predictions across all the individual models ( SVM, NN, NB) in the ensemble using a voting mechanism.And this model has high accuracy of predicting the subtype of cancer than individual models.
Boosting
Extreme gradient boosting
The term “gradient boosting” comes from the idea of “boosting” or improving a single weak model by combining it with a number of other weak models in order to generate a collectively strong model. Gradient boosting is an extension of boosting where the process of additively generating weak models is formalized as a gradient descent algorithm over an objective function. Gradient boosting sets targeted outcomes for the next model in an effort to minimize errors. Targeted outcomes for each case are based on the gradient of the error (hence the name gradient boosting) with respect to the prediction.
XGBoost (eXtreme Gradient Boosting) is a machine learning classifier/predictor, which produces a model in a form of an ensemble of weak prediction models. XGBoost helps to reduce overfitting.
## Extreme gradient boosting
xgbGrid <- expand.grid(nrounds = c(1, 10),
max_depth = c(1, 4),
eta = c(.1, .4),
gamma = 0,
colsample_bytree = .7,
min_child_weight = 1,
subsample = c(.8, 1))
cctrl1 <- trainControl(method = "cv", number = 3, returnResamp = "all",
classProbs = TRUE)
train_en<- createDataPartition(data_norm$`PAM50.mRNA`, p = .70, list = FALSE)
trainDF<-data_norm[train_en,]
testDF<-data_norm[-train_en,]
trainDF$`PAM50.mRNA`<- as.factor(trainDF$`PAM50.mRNA`)
xgb <- train(`PAM50.mRNA` ~., data=trainDF,
method = "xgbTree",
trControl = cctrl1,
preProc = c("center", "scale"),
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confusionMatrix(testDF$`PAM50.mRNA`,bpred_xgb)## Confusion Matrix and Statistics
##
## Reference
## Prediction Basal.like HER2.enriched Luminal.A Luminal.B
## Basal.like 3 0 2 0
## HER2.enriched 1 2 0 0
## Luminal.A 0 0 5 1
## Luminal.B 0 0 1 6
##
## Overall Statistics
##
## Accuracy : 0.7619
## 95% CI : (0.5283, 0.9178)
## No Information Rate : 0.381
## P-Value [Acc > NIR] : 0.0004398
##
## Kappa : 0.6698
##
## Mcnemar's Test P-Value : NA
##
## Statistics by Class:
##
## Class: Basal.like Class: HER2.enriched Class: Luminal.A
## Sensitivity 0.7500 1.00000 0.6250
## Specificity 0.8824 0.94737 0.9231
## Pos Pred Value 0.6000 0.66667 0.8333
## Neg Pred Value 0.9375 1.00000 0.8000
## Prevalence 0.1905 0.09524 0.3810
## Detection Rate 0.1429 0.09524 0.2381
## Detection Prevalence 0.2381 0.14286 0.2857
## Balanced Accuracy 0.8162 0.97368 0.7740
## Class: Luminal.B
## Sensitivity 0.8571
## Specificity 0.9286
## Pos Pred Value 0.8571
## Neg Pred Value 0.9286
## Prevalence 0.3333
## Detection Rate 0.2857
## Detection Prevalence 0.3333
## Balanced Accuracy 0.8929I have also built extreme gradient boosting model. This model has an accuracy of 61.9%
Therefore, Stacked ensemble stacked ensemble (SVM, NN, NB) model works better than extereme gradient boosting model
Conclusion
It’s really intriguing to me that ML techniques may be used to identify a group of predictor proteins that outperform proteins known to be linked to the genetic test that determines the classification in terms of identifying cancer subtypes.
The lasso-selected variables consistently outperformed the PAM50 ones, but no other methods produced classification results that were more accurate than the SVM NN and stacked ensemble (SVM, NN, NB).